Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is: Can LLMs beat traditional ML models in clinical prediction? Thus, we build a new benchmark ClinicalBench to comprehensively study the clinical predictive modeling capacities of both general-purpose and medical LLMs, and compare them with traditional ML models. ClinicalBench embraces three common clinical prediction tasks, two databases, 14 general-purpose LLMs, 8 medical LLMs, and 11 traditional ML models. Through extensive empirical investigation, we discover that both general-purpose and medical LLMs, even with different model scales, diverse prompting or fine-tuning strategies, still cannot beat traditional ML models in clinical prediction yet, shedding light on their potential deficiency in clinical reasoning and decision-making. We call for caution when practitioners adopt LLMs in clinical applications. ClinicalBench can be utilized to bridge the gap between LLMs' development for healthcare and real-world clinical practice.
Following PyHealth, every task predicts from demographics (gender, age) plus diagnoses, procedures, medications and drugs for the current visit. AUROC is also reported.
11 traditional ML models
14 general-purpose LLMs
8 medical LLMs
Only open-source LLMs are evaluated: closed-source models such as GPT-4 are prohibited on MIMIC-III and MIMIC-IV under the PhysioNet credentialed data use policy.
Click a finding to expand the evidence behind it.
We compare 9 general-purpose and 6 medical LLMs at around the 7B scale against 11 traditional ML models, then push further by sweeping decoding temperatures and scaling parameters up to 70B.
Directly prompting on MIMIC-III, averaged over five runs.
| Method | Length-of-Stay Prediction | Mortality Prediction | Readmission Prediction | |||
|---|---|---|---|---|---|---|
| Macro F1 (%) | AUROC (%) | F1 (%) | AUROC (%) | F1 (%) | AUROC (%) | |
| Majority | 23.37(23.37, 23.37) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) |
| Minority | 10.72(10.72, 10.72) | 50.00(50.00, 50.00) | 20.88(20.88, 20.88) | 50.00(50.00, 50.00) | 13.64(13.64, 13.64) | 50.00(50.00, 50.00) |
| Traditional ML Models | ||||||
| XGBoost | 67.94(67.87, 68.01) | 93.83(93.78, 93.88) | 65.75(65.56, 65.94) | 95.97(95.93, 96.01) | 19.92(19.75, 20.09) | 69.24(68.75, 69.73) |
| Logistic Regression | 66.52(66.43, 66.61) | 93.09(92.99, 93.19) | 63.09(62.96, 63.22) | 94.59(94.53, 94.65) | 19.88(19.73, 20.03) | 69.19(68.85, 69.53) |
| Decision Tree | 59.14(59.04, 59.24) | 85.22(85.14, 85.30) | 52.17(52.05, 52.29) | 84.18(83.93, 84.43) | 16.1(15.94, 16.26) | 57.57(57.05, 58.09) |
| Random Forest | 61.14(61.09, 61.19) | 91.23(91.15, 91.31) | 54.23(54.10, 54.36) | 91.65(91.56, 91.74) | 18.78(18.64, 18.92) | 69.3(69.04, 69.56) |
| AdaBoost | 66.6(66.48, 66.72) | 92.91(92.85, 92.97) | 61.19(60.99, 61.39) | 93.62(93.54, 93.70) | 17.92(17.75, 18.09) | 64.51(64.20, 64.82) |
| SVM | 70.28(70.20, 70.36) | 94.77(94.71, 94.83) | 63.97(63.81, 64.13) | 95.69(95.65, 95.73) | 20.17(20.03, 20.31) | 71.74(71.36, 72.12) |
| Naive Bayes | 42.93(42.84, 43.02) | 61.55(61.43, 61.67) | 23.43(23.41, 23.45) | 56.85(56.80, 56.90) | 16.86(16.77, 16.95) | 58.76(58.56, 58.96) |
| KNN | 44.27(44.20, 44.34) | 73.72(73.59, 73.85) | 44.52(44.37, 44.67) | 85.39(85.34, 85.44) | 15.09(14.91, 15.27) | 59.45(58.73, 60.17) |
| MLP | 63.54(63.47, 63.61) | 90.61(90.52, 90.70) | 59.32(59.16, 59.48) | 93.01(92.92, 93.10) | 18.86(18.69, 19.03) | 67.47(67.22, 67.72) |
| Transformer | 63.56(63.45, 63.67) | 89.58(89.50, 89.66) | 57.38(57.06, 57.70) | 93.35(93.26, 93.44) | 18.97(18.81, 19.13) | 67.27(67.01, 67.53) |
| RNN | 64.23(64.12, 64.34) | 90.08(90.01, 90.15) | 59.03(58.74, 59.32) | 91.97(91.89, 92.05) | 19.40(19.26, 19.54) | 69.32(69.12, 69.52) |
| General-purpose Large Language Models | ||||||
| Llama3-8B | 25.78(25.72, 25.84) | 83.82(83.74, 83.90) | 25.81(25.78, 25.84) | 85.40(85.33, 85.47) | 13.64(13.64, 13.64) | 43.00(42.50, 43.50) |
| Mistral-v0.3-7B | 11.59(11.57, 11.61) | 69.73(69.64, 69.82) | 38.11(38.06, 38.16) | 86.84(86.80, 86.88) | 13.44(13.38, 13.50) | 47.16(46.69, 47.63) |
| Gemma2-9B | 17.30(17.26, 17.34) | 79.19(79.08, 79.30) | 43.03(42.96, 43.10) | 86.46(86.41, 86.51) | 13.66(13.66, 13.66) | 47.14(46.87, 47.41) |
| Qwen2-7B | 12.17(12.14, 12.20) | 73.97(73.82, 74.12) | 34.98(34.81, 35.15) | 85.97(85.91, 86.03) | 2.14(1.88, 2.40) | 44.04(43.61, 44.47) |
| Yi-v1.5-9B | 10.89(10.88, 10.90) | 74.51(74.41, 74.61) | 28.75(28.72, 28.78) | 76.05(75.96, 76.14) | 11.35(11.23, 11.47) | 45.02(44.76, 45.28) |
| Vicuna-v1.5-7B | 22.05(22.01, 22.09) | 73.20(73.11, 73.29) | 20.91(20.91, 20.91) | 77.90(77.80, 78.00) | 13.64(13.64, 13.64) | 39.51(39.18, 39.84) |
| Phi3.5-mini-3.8B | 13.17(13.14, 13.20) | 73.67(73.63, 73.71) | 26.45(26.43, 26.47) | 80.37(80.30, 80.44) | 12.40(12.30, 12.50) | 46.64(46.37, 46.91) |
| InternLM2.5-7B | 11.53(11.52, 11.54) | 82.38(82.28, 82.48) | 33.89(33.57, 34.21) | 83.89(83.81, 83.97) | 0.00(0.00, 0.00) | 45.84(45.51, 46.17) |
| MiniCPM3-4B | 22.25(22.21, 22.29) | 75.95(75.86, 76.04) | 24.01(24.00, 24.02) | 86.28(86.21, 86.35) | 13.36(13.31, 13.41) | 42.23(41.95, 42.51) |
| Medical Large Language Models | ||||||
| Meditron-7B | 33.43(33.39, 33.47) | 76.63(76.51, 76.75) | 20.88(20.88, 20.88) | 77.26(77.20, 77.32) | 13.64(13.64, 13.64) | 44.93(44.43, 45.43) |
| Medllama3-8B | 14.88(14.85, 14.91) | 78.27(78.22, 78.32) | 24.12(24.11, 24.13) | 78.74(78.66, 78.82) | 13.64(13.64, 13.64) | 49.80(49.24, 50.36) |
| BioMistral-7B | 21.96(21.89, 22.03) | 40.94(40.73, 41.15) | 30.53(30.45, 30.61) | 77.88(77.81, 77.95) | 13.37(13.28, 13.46) | 40.93(40.54, 41.32) |
| Med42-8B | 12.82(12.80, 12.84) | 84.76(84.70, 84.82) | 28.02(27.98, 28.06) | 82.55(82.46, 82.64) | 13.62(13.60, 13.64) | 42.95(42.56, 43.34) |
| BioMedGPT-7B | 14.11(14.11, 14.11) | 61.54(61.45, 61.63) | 22.55(22.54, 22.56) | 78.93(78.82, 79.04) | 13.72(13.65, 13.79) | 42.89(42.67, 43.11) |
| Internist-7B | 11.71(11.68, 11.74) | 81.10(81.04, 81.16) | 37.97(37.92, 38.02) | 87.25(87.20, 87.30) | 12.07(11.90, 12.24) | 47.67(47.36, 47.98) |
Performance of LLMs and traditional ML models on the three clinical prediction tasks. Average over five runs. The numbers in brackets are the 95% confidence interval computed from the five runs. (Macro) F1 and AUROC are used because of label imbalance. Darker and shading indicates scores closer to 100%. Majority / Minority denote always predicting the majority / minority class. Results should be interpreted keeping in mind that ICD-based features are administrative rather than purely clinical data.
| Method | Length-of-Stay Prediction | Mortality Prediction | Readmission Prediction | |||
|---|---|---|---|---|---|---|
| MIMIC-III | MIMIC-IV | MIMIC-III | MIMIC-IV | MIMIC-III | MIMIC-IV | |
| Traditional ML Models | ||||||
| XGBoost | 67.98 | 64.19 | 64.46 | 48.15 | 19.33 | 27.73 |
| SVM | 70.63 | 66.43 | 63.10 | 46.27 | 18.85 | 28.27 |
| Transformer | 64.11 | 54.97 | 56.46 | 42.81 | 17.80 | 26.40 |
| RNN | 64.76 | 59.89 | 56.32 | 48.9 | 19.96 | 25.58 |
| General-purpose Large Language Models | ||||||
| Llama3-8B | 25.38 | 17.73 | 26.06 | 15.12 | 13.64 | 25.05 |
| Llama3-70B | 18.66 | 23.01 | 30.33 | 18.43 | 12.40 | 22.81 |
| Qwen2-0.5B | 5.11 | 5.78 | 1.94 | 3.11 | 0.00 | 0.60 |
| Qwen2-1.5B | 23.37 | 29.55 | 20.91 | 6.88 | 13.72 | 24.82 |
| Qwen2-7B | 11.96 | 6.48 | 35.65 | 43.53 | 4.26 | 0.29 |
| Yi-v1.5-6B | 25.81 | 30.18 | 28.35 | 18.26 | 13.69 | 24.66 |
| Yi-v1.5-9B | 10.94 | 6.96 | 28.82 | 16.55 | 10.74 | 20.73 |
| Yi-v1.5-34B | 18.92 | 18.42 | 41.78 | 33.63 | 13.46 | 24.12 |
| Medical Large Language Models | ||||||
| Meditron-7B | 33.46 | 26.90 | 20.88 | 6.70 | 13.64 | 24.92 |
| Meditron-70B | 30.15 | 35.67 | 43.16 | 47.93 | 8.67 | 9.12 |
| Med42-8B | 12.69 | 9.97 | 28.59 | 16.79 | 13.59 | 25.06 |
| Med42-70B | 15.43 | 22.95 | 42.96 | 31.21 | 12.99 | 23.87 |
Parameter scaling within model series. Length-of-Stay uses Macro F1 (%), the other two tasks use F1 (%). Scaling from 0.5B to 70B does not reliably help. Qwen2-1.5B beats both Qwen2-0.5B and Qwen2-7B on Length-of-Stay and Readmission, and Yi-v1.5-6B beats the 9B and 34B models on Length-of-Stay in both databases.
Zero-shot Chain-of-Thought, Self-Reflection, Role-Playing and In-Context Learning are applied to a general-purpose series (Llama3) and a medical series (Meditron), each at both the 7–8B and 70B scales.
| Method | Length-of-Stay Prediction | Mortality Prediction | Readmission Prediction | |||
|---|---|---|---|---|---|---|
| MIMIC-III | MIMIC-IV | MIMIC-III | MIMIC-IV | MIMIC-III | MIMIC-IV | |
| Traditional ML Models | ||||||
| XGBoost | 63.88 | 57.25 | 48.04 | 40.00 | 18.80 | 26.95 |
| SVM | 66.16 | 59.08 | 56.47 | 36.59 | 18.48 | 26.52 |
| Transformer | 61.49 | 53.39 | 51.85 | 29.13 | 17.32 | 23.21 |
| RNN | 64.26 | 60.09 | 58.96 | 49.80 | 20.80 | 26.24 |
| General-purpose Large Language Models | ||||||
| Llama3-8B | 26.36 | 16.14 | 25.84 | 14.35 | 13.78 | 25.40 |
| + Chain-of-Thought | 14.76 | 9.22 | 23.18 | 10.81 | 12.05 | 22.90 |
| + Self-Reflection | 25.88 | 17.53 | 24.10 | 14.29 | 15.70 | 22.80 |
| + Role-Playing | 27.44 | 17.30 | 25.44 | 14.11 | 13.78 | 24.87 |
| + In-Context Learning | 51.77 | 54.72 | 28.02 | 14.85 | 11.97 | 24.46 |
| Llama3-70B | 18.88 | 21.76 | 29.82 | 17.80 | 13.72 | 22.03 |
| + Chain-of-Thought | 10.00 | 9.02 | 0.00 | 0.00 | 0.00 | 0.00 |
| + Self-Reflection | 25.70 | 26.28 | 21.13 | 7.28 | 13.81 | 25.13 |
| + Role-Playing | 11.68 | 11.37 | 23.34 | 11.68 | 13.83 | 24.95 |
| + In-Context Learning | 41.04 | 47.52 | 25.22 | 12.41 | 12.47 | 25.43 |
| Medical Large Language Models | ||||||
| Meditron-7B | 35.88 | 27.53 | 20.79 | 6.58 | 13.78 | 24.87 |
| + Chain-of-Thought | 16.04 | 12.58 | 13.90 | 5.95 | 12.83 | 24.34 |
| + Self-Reflection | 18.36 | 10.40 | 20.47 | 6.60 | 13.78 | 24.91 |
| + Role-Playing | 27.73 | 17.54 | 20.79 | 6.58 | 13.78 | 24.87 |
| + In-Context Learning | 14.09 | 14.62 | 23.72 | 11.37 | 11.64 | 23.27 |
| Meditron-70B | 27.23 | 34.52 | 46.15 | 34.48 | 9.64 | 9.90 |
| + Chain-of-Thought | 9.39 | 6.92 | 4.24 | 0.43 | 11.34 | 18.66 |
| + Self-Reflection | 17.72 | 13.51 | 0.00 | 0.00 | 0.00 | 1.69 |
| + Role-Playing | 46.46 | 56.06 | 14.08 | 34.48 | 0.00 | 2.70 |
| + In-Context Learning | 55.40 | 50.79 | 27.40 | 11.26 | 8.56 | 19.35 |
LLMs with prompt engineering vs. traditional ML models. Experiments on sampled MIMIC-III and MIMIC-IV test sets. Length-of-Stay uses Macro F1 (%), the other two tasks use F1 (%). In-Context Learning is the one strategy that clearly helps, and only on Length-of-Stay Prediction. Chain-of-Thought hurts almost everywhere, driving Llama3-70B to 0.00 F1 on Mortality and Readmission Prediction in both databases.
Four LLMs are fine-tuned with two LoRA variants, one applied to all layers and one to the last layer only. Both use exactly the same training/validation/test split as the traditional ML models, run for 20 epochs, and select the checkpoint on the validation set.
High scores on medical licensing exams and question-answering benchmarks such as MedQA and MedMCQA suggest that LLMs carry clinician-level general medical knowledge. Whether that knowledge supports clinical reasoning and decision-making in real-world, complex scenarios has remained largely unexplored. By benchmarking predictive modeling head-to-head against traditional ML models, ClinicalBench sheds light on a potential deficiency.
We acknowledge the importance of data privacy in clinical research and followed the data use guidelines of the MIMIC datasets.
The exact prompt fed to every LLM, and what four representative models answered. Length-of-Stay expects 1/2/3. Mortality and Readmission expect 0/1. Chain-of-Thought and Self-Reflection additionally ask for reasoning, which is where several models drift off-task.
Patient information: Age: 62 Gender: male Conditions: Streptococcal septicemia, Pressure ulcer, lower back, Acute osteomyelitis, pelvic region and thigh, Unknown Diagnosis, Cocaine abuse, unspecified, Acute kidney failure, unspecified, Hemorrhage complicating a procedure, Hyperpotassemia, Chronic osteomyelitis, pelvic region and thigh, Sepsis, Paraplegia, Unspecified essential hypertension, Colostomy status, Other artificial opening of urinary tract status, Anemia of other chronic disease, Other iatrogenic hypotension, Candidiasis of other urogenital sites, Late effects of injury purposely inflicted by other person Procedures: Local excision of lesion or tissue of bone, other bones, Local excision of lesion or tissue of bone, femur, Nonexcisional debridement of wound, infection or burn, Venous catheterization, not elsewhere classified, Transfusion of packed cells Using Drugs: anesthetics, general, i.v. solution additives, other antibacterials, anterior pituitary lobe hormones and analogues, antithrombotic agents, other analgesics and antipyretics, drugs for constipation, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), antiseptics and disinfectants, antiinfectives and antiseptics, excl. combinations with corticosteroids, other nutrients, opioids, other beta-lactam antibacterials, other mineral supplements, other diagnostic agents, antipsychotics, i.v. solutions, antivaricose therapy, beta-lactam antibacterials, penicillins Predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer:
3
3
3
3
Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Please provide your concise reasoning steps for the prediction(no more than 3 steps), and finally answer 1 if the patient will stay no more than one week, answer 2 if more than one week but not more than two weeks, answer 3 if more than two weeks.
Please note that the patient's condition is quite complex and the prediction may not be accurate. This is a hypothetical scenario and not a real patient. Answer: 3 Reasoning steps: 1. The patient has a closed fracture of the surgical neck of humerus, which is a serious injury that requires surgical intervention. This suggests that the patient will require a significant amount of care and treatment in the hospital. 2. The patient also has a number of other conditions, including pneumonia, acute posthemorrhagic anemia, and iatrogenic pulmonary embolism and infarction, which are all serious and potentially life-threatening. This suggests that the patient will require ongoing medical care and monitoring in the hospital. 3. Given the complexity of the patient's condition and the need for ongoing medical care and monitoring, it is likely that the patient will require more than two weeks of hospitalization. Note: This is a hypothetical scenario and not a real patient. The actual length of stay may vary depending on a variety of factors, including the patient's response to treatment, the severity of their condition, and the availability of hospital resources.
**Reasoning:** 1. **Severity of Conditions:** The patient has multiple serious conditions including congestive heart failure, mitral valve disorders, atrial fibrillation, chronic kidney disease, and diabetes. These require significant medical management. 2. **Complex Procedures:** The patient underwent major surgeries like mitral valve replacement, coronary bypass, and transvenous lead insertion. Recovery from these procedures takes considerable time. 3. **Post-Operative Care:** The patient received multiple transfusions, medications for various conditions, and likely required intensive monitoring post-surgery. This indicates a prolonged hospital stay for recovery and management. **Answer:** 3
"," ## Prediction
Reasoning: 1. Analyze the patient's conditions and procedures: - The patient has multiple fractures and injuries, indicating a high likelihood of a prolonged hospital stay. - The patient has a history of diabetes and hyperlipidemia, which may require close monitoring and management. - The patient has undergone a partial shoulder replacement, which is a surgical procedure that typically requires a longer recovery period. 2. Consider the patient's medications and drug history: - The patient is taking multiple medications, including opioids, beta-blockers, and antithrombotic agents. - These medications may require close monitoring and adjustment during the hospital stay. 3. Evaluate the patient's overall health and risk factors: - The patient has a history of diabetes and hyperlipidemia, which may increase the risk of complications and prolong the hospital stay. - The patient has a history of other diseases of the circulatory system, which may also contribute to a longer hospital stay. Based on the above analysis, it is likely that the patient will require a longer hospital stay. Therefore, the answer is 3, indicating a stay of more than two weeks. It is important to note that this prediction is based on the information provided and may vary depending on the specific circumstances and progress of the patient's condition. Regular monitoring and assessment by healthcare professionals will be necessary to determine the actual duration of the hospital stay. Please note that this is a prediction based on the given information and may not be accurate in all cases. It is important to consider individual patient factors and consult with healthcare professionals for a more accurate assessment.
Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. First answer with a number. Then conduct a concise reflection. Finally output your answer again with a number.
Answer: 3 Reflection: The patient has a complex medical history with multiple conditions, including a closed fracture of the surgical neck of humerus, pneumonia, and iatrogenic pulmonary embolism. They have undergone a partial shoulder replacement and have received multiple medications. Given the severity of their conditions and the need for ongoing medical care, it is likely that they will require a prolonged hospital stay of more than two weeks. Answer: 3 ```python ......
**Answer:** 3 **Reflection:** The patient presents with multiple severe injuries, including a closed fracture of the surgical neck of the humerus, facial bones, and a hematoma complicating a procedure. These injuries necessitate surgical intervention (partial shoulder replacement) and extensive post-operative care. The patient also has a history of diabetes, emphysema, and hypertension, which can complicate recovery. The presence of pneumonia and acute posthemorrhagic anemia further suggests a prolonged hospital stay. **Answer:** }3
Answer 1 Answer 2 Answer 3 Answer 1 Answer 2 Answer 3 Answer 1 Answer 2 Answer 3 Answer 1 Answer 2 Answer 3 Answer 1 Answer 2 Answer 3
Reasoning:To predict the number of weeks of stay in hospital, we need to analyze the patient's conditions, procedures, and medications. 1. Conditions: The patient has multiple conditions, including a closed fracture of the surgical neck of the humerus, other emphysema, closed fractures of other facial bones, closed fractures of the malar and maxillary bones, hematoma complicating a procedure, iatrogenic pulmonary embolism and infarction, acute posthemorrhagic anemia, pneumonia, and fall from other slipping, tripping, or stumbling. These conditions indicate that the patient may require a longer stay in the hospital. 2. Procedures: The patient has undergone a partial shoulder replacement and a transfusion of packed cells. These procedures may require a longer recovery period and may contribute to a longer hospital stay. 3. Medications: The patient is using various medications, including i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, insulins and analogues, blood glucose lowering drugs, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation. These medications may also contribute to a longer hospital stay. Based on the patient's conditions, procedures, and medications, it is likely that the patient will require a longer stay in the hospital. Therefore, the correct answer is 3, indicating a stay of more......
Imagine that you are a doctor. Today, you're seeing a patient with the following profile: Patient information: Age: 62 Gender: male Conditions: Streptococcal septicemia, Pressure ulcer, lower back, Acute osteomyelitis, pelvic region and thigh, Unknown Diagnosis, Cocaine abuse, unspecified, Acute kidney failure, unspecified, Hemorrhage complicating a procedure, Hyperpotassemia, Chronic osteomyelitis, pelvic region and thigh, Sepsis, Paraplegia, Unspecified essential hypertension, Colostomy status, Other artificial opening of urinary tract status, Anemia of other chronic disease, Other iatrogenic hypotension, Candidiasis of other urogenital sites, Late effects of injury purposely inflicted by other person Procedures: Local excision of lesion or tissue of bone, other bones, Local excision of lesion or tissue of bone, femur, Nonexcisional debridement of wound, infection or burn, Venous catheterization, not elsewhere classified, Transfusion of packed cells Using Drugs: anesthetics, general, i.v. solution additives, other antibacterials, anterior pituitary lobe hormones and analogues, antithrombotic agents, other analgesics and antipyretics, drugs for constipation, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), antiseptics and disinfectants, antiinfectives and antiseptics, excl. combinations with corticosteroids, other nutrients, opioids, other beta-lactam antibacterials, other mineral supplements, other diagnostic agents, antipsychotics, i.v. solutions, antivaricose therapy, beta-lactam antibacterials, penicillins Predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer:
3
3
3
3
Patient information: Age: 74 Gender: male Conditions: Subendocardial infarction, initial episode of care, Intestinal infection due to Clostridium difficile, Congestive heart failure, unspecified, Coronary atherosclerosis of native coronary artery, Unspecified essential hypertension, Pure hypercholesterolemia, Abdominal aneurysm without mention of rupture, Percutaneous transluminal coronary angioplasty status, Personal history of malignant neoplasm of large intestine Procedures: Unknown Procedure, Combined right and left heart cardiac catheterization, Coronary arteriography using two catheters Using Drugs: i.v. solution additives, cardiac stimulants excl. cardiac glycosides, other nutrients, antithrombotic agents, potassium, expectorants, excl. combinations with cough suppressants, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), hypnotics and sedatives, other analgesics and antipyretics, opioids, lipid modifying agents, plain, belladonna and derivatives, plain, other mineral supplements, other diagnostic agents, agents against amoebiasis and other protozoal diseases, anxiolytics, quinolone antibacterials, ace inhibitors, plain, beta blocking agents, other antibacterials Predict the number of weeks of stay in hospital. Answer 1 if no more than one , Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer: 1 Patient information: Age: 36 Gender: male Conditions: Thoracic aneurysm without mention of rupture, Aortic valve disorders, Congenital insufficiency of aortic valve, Cardiac complications, not elsewhere classified, Other specified cardiac dysrhythmias, Unspecified essential hypertension Procedures: Open and other replacement of aortic valve, Resection of vessel with replacement, thoracic vessels, Other operations on vessels of heart, Extracorporeal circulation auxiliary to open heart surgery Using Drugs: drugs for functional gastrointestinal disorders, antiarrhythmics, class i and iii, drugs for constipation, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), other analgesics and antipyretics, antiinflammatory and antirheumatic products, non-steroids, opioids, other nutrients, anesthetics, general, antiinfectives and antiseptics, excl. combinations with corticosteroids, calcium, arteriolar smooth muscle, agents acting on, cardiac stimulants excl. cardiac glycosides, i.v. solution additives, other beta-lactam antibacterials, insulins and analogues, propulsives, hypnotics and sedatives, other mineral supplements, other diagnostic agents, beta blocking agents, high-ceiling diuretics, potassium, antithrombotic agents, antacids, urologicals, throat preparations, ace inhibitors, plain, angiotensin ii receptor blockers (arbs), plain Predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer: 2 Input (Cont.): Patient information: Age: 73 Gender: male Conditions: Closed fracture of shaft of femur, Acute posthemorrhagic anemia, Subendocardial infarction, initial episode of care, Other postoperative infection, Unspecified septicemia, Severe sepsis, Septic shock, Unknown Diagnosis, Cardiogenic shock, Cardiac complications, not elsewhere classified, Paroxysmal ventricular tachycardia, Ventricular fibrillation, Congestive heart failure, unspecified, Atrial fibrillation, Unknown Diagnosis, Pneumonia, organism unspecified, Unspecified fall, Coronary atherosclerosis of native coronary artery, Cardiac pacemaker in situ Procedures: Open reduction of fracture with internal fixation, femur, Closed reduction of fracture without internal fixation, femur, Left heart cardiac catheterization, Coronary arteriography using two catheters, Continuous invasive mechanical ventilation for 96 consecutive hours or more, Venous catheterization, not elsewhere classified, Enteral infusion of concentrated nutritional substances Using Drugs: i.v. solution additives, opioids, other analgesics and antipyretics, beta blocking agents, selective calcium channel blockers with direct cardiac effects, antithrombotic agents, vasodilators used in cardiac diseases, agents for treatment of hemorrhoids and anal fissures for topical use, angiotensin ii receptor blockers (arbs), plain, antidepressants, drugs for constipation, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), anti-dementia drugs, lipid modifying agents, plain, antipsychotics, antipruritics, incl. antihistamines, anesthetics, etc., anxiolytics, antiarrhythmics, class i and iii, quinolone antibacterials, antacids, urologicals, expectorants, excl. combinations with cough suppressants, antiinfectives and antiseptics, excl. combinations with corticosteroids, calcium, other beta-lactam antibacterials, i.v. solutions, antivaricose therapy, other mineral supplements, other diagnostic agents, high-ceiling diuretics, anesthetics, general, muscle relaxants, peripherally acting agents, other antibacterials, other nutrients, hypnotics and sedatives, beta-lactam antibacterials, penicillins, cardiac stimulants excl. cardiac glycosides, anterior pituitary lobe hormones and analogues, other drugs for obstructive airway diseases, inhalants, adrenergics, inhalants, potassium, antifungals for topical use, ace inhibitors, plain Predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer: 3 Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Input (Cont.): Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number. Answer:
2
2
2
1
Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Will the patient die because of the above situation? Answer 1 if yes, 0 if no. Answer with only the number. Answer:
1
0
1
0
Patient information: Age: 61 Gender: male Conditions: Unspecified septicemia, Acute pancreatitis, Alcohol abuse, unspecified, Acute kidney failure, unspecified, Acute and subacute necrosis of liver, Acidosis, Defibrination syndrome, Shock, unspecified, Unknown Diagnosis, Unspecified protein-calorie malnutrition, Acute respiratory failure, Severe sepsis, Anemia, unspecified, Acute alcoholic hepatitis, Personal history of noncompliance with medical treatment, presenting hazards to health, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Long-term (current) use of insulin, Abdominal pain, other specified site Procedures: Continuous invasive mechanical ventilation for less than 96 consecutive hours, Insertion of endotracheal tube, Arterial catheterization, Venous catheterization, not elsewhere classified, Transfusion of packed cells Using Drugs: insulins and analogues, other nutrients, i.v. solution additives, other beta-lactam antibacterials, anxiolytics, anterior pituitary lobe hormones and analogues, quinolone antibacterials, other antibacterials, vitamin b1, plain and in combination with vitamin b6 and b12, other diagnostic agents, vitamin b12 and folic acid, vitamin k and other hemostatics, other mineral supplements, beta-lactam antibacterials, penicillins, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), hypnotics and sedatives, anesthetics, general, posterior pituitary lobe hormones, calcium, cardiac stimulants excl. cardiac glycosides Will the patient die because of the above situation? Please provide your concise reasoning steps for the prediction(no more than 3 steps), and finally answer 1 if the patient will die and 0 otherwise.
Here is the reasoning steps: Step 1: The patient has severe sepsis, acute kidney failure, and liver failure, which are all life-threatening conditions. Step 2: The patient also has a history of non-compliance with medical treatment, which may worsen the patient's condition. Step 3: Given the severity of the patient's conditions and the potential for non-compliance with treatment, it is likely that the patient will die. Answer: 1
**Reasoning:** 1. **Severe Sepsis and Multi-Organ Failure:** The patient presents with severe sepsis, a life-threatening condition, and multiple organ failures (pancreas, liver, kidneys, respiratory system). 2. **Complicating Factors:** Alcohol abuse, diabetes, and malnutrition further weaken the patient's immune system and ability to fight infection. 3. **Aggressive Treatment:** The patient is receiving intensive care interventions (ventilation, transfusions, multiple medications), indicating a critical condition requiring urgent medical attention. **Answer:** 1 **Disclaimer:** I am an AI chatbot and cannot provide medical advice. This prediction is based on the information provided and should not be considered a definitive prognosis. The patient's actual outcome depends on various factors and individual medical management.
## 1. The patient is 83 years old. ## 2. The patient has pneumonia. ## 3. The patient has organism unspecified. ## 4. The patient has postinflammatory pulmonary fibrosis. ## 5. The patient has atrial fibrillation. ## 6. The patient has congestive heart failure, unspecified. ## 7. The patient has acute respiratory failure. ## 8. The patient has coronary atherosclerosis of native coronary artery. ## 9. The patient has esophageal reflux. ## 10. The patient is using macrolides, lincosamides and streptogramins. ## 11. The patient is using other beta-lactam antibacterials. ## 12. The patient is using expectorants, excl. combinations with cough suppressants.
Reasoning: 1. Assess the patient's overall condition: The patient has multiple comorbidities, including septicemia, acute pancreatitis, alcohol abuse, acute kidney failure, acute and subacute necrosis of liver, acidosis, defibrination syndrome, shock, unspecified, unknown diagnosis, unspecified protein-calorie malnutrition, acute respiratory failure, severe sepsis, anemia, unspecified, acute alcoholic hepatitis, personal history of noncompliance with medical treatment, presenting hazards to health, diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, long-term (current) use of insulin, and abdominal pain. 2. Consider the patient's medical history and current condition: The patient has a history of noncompliance with medical treatment, presenting hazards to health, and long-term use of insulin. The patient also has multiple comorbidities, including septicemia, acute pancreatitis, alcohol abuse, acute kidney failure, acute and subacute necrosis of liver, acidosis, defibrination syndrome, shock, unspecified, unknown diagnosis, unspecified protein-calorie malnutrition, acute respiratory failure, severe sepsis, anemia, unspecified, acute alcoholic hepatitis, diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, and abdominal pain. 3. Evaluate the patient's current treatment and procedures: The patient has undergone continuous invasive mechanical ventilation for less than 96 consecutive hours, insertion of an endotracheal tube, arterial catheterization, venous catheterization, not elsewhere classified, transfusion of packed cells, and has been using various medications, including insulins and analogues, other nutrients, i.v. solution additives, other beta-lactam antibacterials, anxiolytics, anterior pituitary lobe hormones and analogues, quinolone antibacterials, other antibacterials, vitamin b1, plain and in combination with vitamin b.....
Patient information: Age: 61 Gender: male Conditions: Unspecified septicemia, Acute pancreatitis, Alcohol abuse, unspecified, Acute kidney failure, unspecified, Acute and subacute necrosis of liver, Acidosis, Defibrination syndrome, Shock, unspecified, Unknown Diagnosis, Unspecified protein-calorie malnutrition, Acute respiratory failure, Severe sepsis, Anemia, unspecified, Acute alcoholic hepatitis, Personal history of noncompliance with medical treatment, presenting hazards to health, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Long-term (current) use of insulin, Abdominal pain, other specified site Procedures: Continuous invasive mechanical ventilation for less than 96 consecutive hours, Insertion of endotracheal tube, Arterial catheterization, Venous catheterization, not elsewhere classified, Transfusion of packed cells Using Drugs: insulins and analogues, other nutrients, i.v. solution additives, other beta-lactam antibacterials, anxiolytics, anterior pituitary lobe hormones and analogues, quinolone antibacterials, other antibacterials, vitamin b1, plain and in combination with vitamin b6 and b12, other diagnostic agents, vitamin b12 and folic acid, vitamin k and other hemostatics, other mineral supplements, beta-lactam antibacterials, penicillins, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), hypnotics and sedatives, anesthetics, general, posterior pituitary lobe hormones, calcium, cardiac stimulants excl. cardiac glycosides Will the patient die because of the above situation? Answer 1 if yes, 0 if no. First answer with a number. Then conduct a concise reflection. Finally output your answer again with a number.
Answer: 1
Reflection: The patient has a multitude of severe conditions, including septicemia, acute pancreatitis, and acute kidney failure, which are all life-threatening. Additionally, the patient has a history of noncompliance with medical treatment, which may have contributed to the severity of their condition. The patient is also receiving multiple medications, including antibiotics, insulin, and other treatments, which may be helping to manage their condition. However, the patient's overall prognosis is poor, and it is likely that they will die due to the severity of their condition.
Answer: 1
```python
# Define the patient information as a dictionary
patient_info = {
""age"": 61,
......
0 It is impossible to determine with certainty whether a patient will die based solely on the provided information. While the patient presents with a multitude of severe and life-threatening conditions, including septicemia, pancreatitis, kidney failure, liver necrosis, and shock, the course of these conditions and the effectiveness of treatment are crucial factors. The patient's age, medical history, and response to interventions all play a role in the prognosis. A definitive answer requires a comprehensive medical evaluation and ongoing monitoring. }0
## 1 ## 2 ## 3 ## 4 ## 5 ## 6 ## 7 ## 8 ## 9 ## 10 ## 11 ## 12 ## 13 ## 14
Reasoning:The patient in this scenario has multiple medical conditions and has undergone several procedures. The question asks if the patient will die because of these factors. To determine the answer, we need to consider the patient's medical history, current conditions, and the procedures they have undergone. The patient has a history of alcohol abuse, acute pancreatitis, acute kidney failure, acute and subacute necrosis of the liver, acidosis, defibrination syndrome, shock, unspecified protein-calorie malnutrition, acute respiratory failure, severe sepsis, anemia, acute alcoholic hepatitis, and a personal history of noncompliance with medical treatment. The patient has also undergone several procedures, including continuous invasive mechanical ventilation for less than 96 consecutive hours, insertion of an endotracheal tube, arterial catheterization, venous catheterization, and transfusion of packed cells. Given the patient's multiple medical conditions and the procedures they have undergone, it is likely that the patient's overall health is compromised. The combination of these factors increases the risk of complications and potentially death. Therefore, the answer is 1, indicating that the patient may die due to the above situation. Reflecting on the answer, it is important to consider the patient's medical history, current conditions, and the procedures they have undergone. The combination of these factors increases the risk of complications and potentially death. It is crucial to provide appropriate medical care and support to the patient to improve their chances of survival. Outputting the answer again with a number, the answer is still 1, indicating that the patient may die due to the above situation. In summary, based on the patient's medical history, current conditions, and the procedures they have undergone, there is a high risk of complications and potentially death. It is important to provide appropriate medical care and support to the patient to improve their chances of survival. The answer is 1. Note: It is important to consider the patient's overall health and medical history when determining the likelihood of death. This answer is based on the information provided in the scenario and may vary in real-life situations. Always consult with a healthcare professional for accurate medical......
Imagine that you are a doctor. Today, you're seeing a patient with the following profile: Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Will the patient die because of the above situation? Answer 1 if yes, 0 if no. Answer with only the number. Answer:
1
1
1
1
Patient information: Age: 43 Gender: female Conditions: Coronary atherosclerosis of native coronary artery, Intermediate coronary syndrome, Diabetes mellitus without mention of complication, type I [juvenile type], not stated as uncontrolled, Unspecified essential hypertension, Pure hypercholesterolemia, Tobacco use disorder Procedures: (Aorto)coronary bypass of two coronary arteries, Left heart cardiac catheterization, Extracorporeal circulation auxiliary to open heart surgery, Coronary arteriography using two catheters, Angiocardiography of left heart structures Using Drugs: other analgesics and antipyretics, antipsychotics, vasodilators used in cardiac diseases, antacids, urologicals, anxiolytics, antidepressants, potassium, iron preparations, lipid modifying agents, plain, beta blocking agents, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), dopaminergic agents, thyroid preparations, opioids, other nutrients, cardiac stimulants excl. cardiac glycosides, drugs for constipation, i.v. solution additives, calcium, propulsives, antiinflammatory and antirheumatic products, non-steroids, other antibacterials, high-ceiling diuretics, antithrombotic agents, other beta-lactam antibacterials, other mineral supplements Will the patient die because of the above situation? Answer 1 if yes, 0 if no. Answer with only the number. Answer: 0 Patient information: Age: 86 Gender: male Conditions: Intracerebral hemorrhage, Pneumonitis due to inhalation of food or vomitus, Unspecified essential hypertension, Aortocoronary bypass status, Coronary atherosclerosis of unspecified type of vessel, native or graft Procedures: Continuous invasive mechanical ventilation for less than 96 consecutive hours Using Drugs: beta blocking agents, vitamin b1, plain and in combination with vitamin b6 and b12, i.v. solution additives, antiepileptics, quinolone antibacterials, other antibacterials, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), other mineral supplements, other diagnostic agents, anxiolytics, anesthetics, general, opioids, antiemetics and antinauseants, hypnotics and sedatives Will the patient die because of the above situation? Answer 1 if yes, 0 if no. Answer with only the number. Answer: 1 Patient information: Age: 73 Gender: female Conditions: Closed fracture of surgical neck of humerus, Other emphysema, Closed fracture of other facial bones, Closed fracture of malar and maxillary bones, Hematoma complicating a procedure, Iatrogenic pulmonary embolism and infarction, Acute posthemorrhagic anemia, Pneumonia, organism unspecified, Fall from other slipping, tripping, or stumbling, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Other specified surgical operations and procedures causing abnormal patient reaction, or later complication, without mention of misadventure at time of operation, Personal history of other diseases of circulatory system, Other and unspecified hyperlipidemia, Unspecified essential hypertension Procedures: Partial shoulder replacement, Transfusion of packed cells Using Drugs: i.v. solution additives, opioids, selective calcium channel blockers with mainly vascular effects, beta blocking agents, high-ceiling diuretics, antithrombotic agents, lipid modifying agents, plain, insulins and analogues, blood glucose lowering drugs, excl. insulins, other analgesics and antipyretics, antiemetics and antinauseants, i.v. solutions, antivaricose therapy, other beta-lactam antibacterials, antipsychotics, anxiolytics, adrenergics, inhalants, other drugs for obstructive airway diseases, inhalants, other antibacterials, corticosteroids for systemic use, plain, other diagnostic agents, vitamin b12 and folic acid, vitamin b1, plain and in combination with vitamin b6 and b12, other mineral supplements, quinolone antibacterials, antidepressants, potassium, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation Will the patient die because of the above situation? Answer 1 if yes, 0 if no. Answer with only the number. Answer:
1
1
1
1
Patient information: Age: 76 Gender: female Conditions: Other specified intestinal obstruction, Malignant neoplasm of anus, unspecified site, Stenosis of rectum and anus, Unknown Diagnosis, Coronary atherosclerosis of native coronary artery, Esophageal reflux, Unspecified essential hypertension, Personal history of malignant neoplasm of rectum, rectosigmoid junction, and anus Procedures: Temporary colostomy, Combined right and left heart cardiac catheterization, Coronary arteriography using two catheters, Venous catheterization, not elsewhere classified Using Drugs: opioids, antithrombotic agents, antiinfectives and antiseptics, excl. combinations with corticosteroids, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), other antibacterials, other beta-lactam antibacterials, beta blocking agents, other mineral supplements, i.v. solution additives, other diagnostic agents, other nutrients, other analgesics and antipyretics, belladonna and derivatives, plain, i.v. solutions, antivaricose therapy, antiemetics and antinauseants, drugs for constipation Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. Answer with only the number. Answer:
1
1
1
1
Patient information: Age: 68 Gender: male Conditions: Malignant neoplasm of other specified sites of bladder, Hemorrhage complicating a procedure Procedures: Other transurethral excision or destruction of lesion or tissue of bladder, Control of (postoperative) hemorrhage of bladder Using Drugs: antiemetics and antinauseants, opioids, belladonna and derivatives, plain, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, other diagnostic agents Will the patient be readmitted to the hospital within two weeks? Please provide your concise reasoning steps for the prediction(no more than 3 steps), and finally answer 1 if the patient will die and 0 otherwise
Here is the solution: Step 1: The patient has undergone a transurethral excision or destruction of lesion or tissue of bladder, which is a surgical procedure. This increases the likelihood of complications and readmission. Step 2: The patient has a history of hemorrhage complicating a procedure, which is a significant complication that may require further treatment and hospitalization. Step 3: The patient is taking a variety of medications, including opioids, antihistamines, and antipsychotics, which may increase the risk of adverse reactions and complications. Answer: 1 (The patient will be readmitted to the hospital within two weeks) Note: This prediction is based on the patient's medical history and procedures, and may not be accurate in all cases. A more detailed review of the patient's medical records and current condition would be necessary to make a more accurate prediction.
**Reasoning:**
1. **Procedure:** The patient underwent a procedure for bladder cancer (""Other transurethral excision or destruction of lesion or tissue of bladder"") and experienced postoperative hemorrhage.
2. **Complications:** Postoperative hemorrhage is a potential complication of bladder cancer procedures and can require further intervention.
3. **Drug Usage:** The patient is on a variety of medications, including opioids and antiemetics, which are often used for pain management and nausea after surgery. This suggests ongoing recovery and potential need for further care.
**Answer:** }1
## 1. The patient is male. ## 2. The patient is 68 years old. ## 3. The patient has a malignant neoplasm of other specified sites of bladder. ## 4. The patient has a hemorrhage complicating a procedure. ## 5. The patient has other transurethral excision or destruction of lesion or tissue of bladder. ## 6. The patient has control of (postoperative) hemorrhage of bladder. ## 7. The patient has antiemetics and antinauseants. ## 8. The patient has opioids. ## 9. The patient has belladonna and derivatives. ## 10. The patient has plain. ......
Reasoning: 1. Assess the patient's condition: The patient is a 68-year-old male with a malignant neoplasm of other specified sites of the bladder and hemorrhage complicating a procedure. This indicates that the patient has undergone a procedure related to the bladder and is experiencing bleeding as a complication. 2. Consider the procedures and medications: The patient has undergone a transurethral excision or destruction of lesion or tissue of the bladder and control of postoperative hemorrhage of the bladder. The patient is also using various medications, including antiemetics and antinauseants, opioids, belladonna and derivatives, antipsychotics, antihistamines, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, and other diagnostic agents. 3. Evaluate the risk factors: The patient's age, gender, and medical history are important factors to consider. The patient is 68 years old, which may increase the risk of complications and readmission. The patient's gender and medical history are also relevant, as they may contribute to the overall risk assessment. 4. Analyze the likelihood of readmission: Based on the patient's condition, procedures, medications, and risk factors, it is important to assess the likelihood of readmission within two weeks. This may involve considering the severity of the patient's condition, the effectiveness of the procedures and medications, and the potential for complications or recurrence of the bleeding. 5. Make the prediction: Based on the analysis, if the likelihood of readmission within two weeks is high, the answer would be 1. If the likelihood is low, the answer would be 0. In this case, the patient's condition, procedures, medications, and risk factors suggest a high likelihood of readmission within two weeks. Therefore, the answer is 1. Note: It is important to consider the specific patient's condition, procedures, medications, and risk factors when making a prediction. This explanation is a general guide and may not apply to every individual case.
Patient information: Age: 68 Gender: male Conditions: Malignant neoplasm of other specified sites of bladder, Hemorrhage complicating a procedure Procedures: Other transurethral excision or destruction of lesion or tissue of bladder, Control of (postoperative) hemorrhage of bladder Using Drugs: antiemetics and antinauseants, opioids, belladonna and derivatives, plain, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, other diagnostic agents Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. First answer with a number. Then conduct a concise reflection. Finally output your answer again with a number.
Please enter your answer. ``` 1 ``` Reflection: Based on the patient's conditions and procedures, it is likely that the patient will be readmitted to the hospital within two weeks. The patient has a malignant neoplasm of the bladder, which may require further treatment or monitoring. Additionally, the patient has undergone a transurethral excision or destruction of lesion or tissue of the bladder, which may lead to complications or side effects that require hospitalization. The patient is also taking a number of medications, which may increase the risk of readmission. Answer: ``` 1 ``` ``` 1
**Reflection:** The patient underwent a procedure for a malignant bladder tumor and experienced post-operative hemorrhage. This suggests a potentially complex recovery and a higher risk of complications. The use of various medications, including opioids and antiemetics, points towards ongoing pain management and potential nausea/vomiting. While it's impossible to say for certain without more information, the patient's condition and the nature of the procedure increase the likelihood of readmission within two weeks. **Answer:** }1
## 1 ## 2 ## 3 ## 4 ## 5 ## 6 ## 7 ## 8 ## 9 ## 10 ## 11 ## 12 ## 13 ## 14 ## 15
Reasoning:The patient in this scenario is a 68-year-old male with a malignant neoplasm of other specified sites of the bladder and hemorrhage complicating a procedure. The patient has undergone a transurethral excision or destruction of lesion or tissue of the bladder and control of postoperative hemorrhage of the bladder. The patient is also using various medications, including antiemetics and antinauseants, opioids, belladonna and derivatives, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, and other diagnostic agents. Based on this information, we need to determine if the patient will be readmitted to the hospital within two weeks. To do this, we need to consider the patient's condition, the procedures performed, and the medications being used. First, the patient has a malignant neoplasm of other specified sites of the bladder, which indicates a serious underlying condition. This suggests that the patient may require ongoing medical management and follow-up. Second, the patient has experienced hemorrhage complicating a procedure. This indicates that the patient may have experienced bleeding during the transurethral excision or destruction of lesion or tissue of the bladder. This complication may require further intervention or monitoring. Third, the patient is using various medications, including antiemetics and antinauseants, opioids, belladonna and derivatives, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, and other diagnostic agents. These medications may have side effects or interactions that could potentially lead to complications or the need for further medical attention. Considering all of these factors, it is likely that the patient may require readmission to the hospital within two weeks......
Imagine that you are a doctor. Today, you're seeing a patient with the following profile: Patient information: Age: 68 Gender: male Conditions: Malignant neoplasm of other specified sites of bladder, Hemorrhage complicating a procedure Procedures: Other transurethral excision or destruction of lesion or tissue of bladder, Control of (postoperative) hemorrhage of bladder Using Drugs: antiemetics and antinauseants, opioids, belladonna and derivatives, plain, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, other diagnostic agents Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. Answer with only the number. Answer:
1
1
1
1
Patient information: Age: 31 Gender: female Conditions: Acute respiratory failure, Myasthenia gravis with (acute) exacerbation, Other specified cardiac dysrhythmias, Diarrhea, Unspecified essential hypertension, Iron deficiency anemia, unspecified Procedures: Continuous invasive mechanical ventilation for 96 consecutive hours or more, Insertion of endotracheal tube, Injection or infusion of immunoglobulin, Non-invasive mechanical ventilation, Enteral infusion of concentrated nutritional substances, Transfusion of packed cells Using Drugs: immunosuppressants, corticosteroids for systemic use, plain, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), other nutrients, i.v. solutions, antivaricose therapy, anticholinergic agents, beta blocking agents, anesthetics, general, parasympathomimetics, other antidiarrheals, calcium, other analgesics and antipyretics, antithrombotic agents, antacids, potassium, i.v. solution additives, sulfonamides and trimethoprim, ace inhibitors, plain, antipropulsives, antidepressants, belladonna and derivatives, plain, anxiolytics, hypnotics and sedatives, other cardiac preparations, antiseptics and disinfectants, antiepileptics Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. Answer with only the number. Answer: 0 Patient information: Age: 62 Gender: male Conditions: Acute myocardial infarction of unspecified site, initial episode of care, Congestive heart failure, unspecified, Unknown Diagnosis, Acute kidney failure with lesion of tubular necrosis, Other and unspecified complications of medical care, not elsewhere classified, Pneumonia, organism unspecified, Late effects of cerebrovascular disease, hemiplegia affecting unspecified side, Unspecified pleural effusion, Unknown Diagnosis, Anticoagulants causing adverse effects in therapeutic use, Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled, Anemia, unspecified, Atrial fibrillation, Attention to tracheostomy, Unspecified essential hypertension, Pure hypercholesterolemia, Coronary atherosclerosis of unspecified type of vessel, native or graft, Aortocoronary bypass status Procedures: Thoracentesis, Enteral infusion of concentrated nutritional substances, Infusion of drotrecogin alfa (activated), Continuous invasive mechanical ventilation for less than 96 consecutive hours Using Drugs: antithrombotic agents, beta blocking agents, antiarrhythmics, class i and iii, other nutrients, lipid modifying agents, plain, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), other analgesics and antipyretics, other beta-lactam antibacterials, other antibacterials, i.v. solution additives, hypnotics and sedatives, anesthetics, general, agents against amoebiasis and other protozoal diseases, quinolone antibacterials, ace inhibitors, plain, high-ceiling diuretics, other antidiarrheals, antacids, insulins and analogues, decongestants and other nasal preparations for topical use, intestinal antiinfectives, calcium, antifungals for topical use, anxiolytics, beta-lactam antibacterials, penicillins, adrenergics, inhalants, antidepressants, potassium Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. Answer with only the number. Answer: 1 Patient information: Age: 68 Gender: male Conditions: Malignant neoplasm of other specified sites of bladder, Hemorrhage complicating a procedure Procedures: Other transurethral excision or destruction of lesion or tissue of bladder, Control of (postoperative) hemorrhage of bladder Using Drugs: antiemetics and antinauseants, opioids, belladonna and derivatives, plain, antipsychotics, antihistamines for systemic use, quinolone antibacterials, i.v. solutions, antivaricose therapy, hypnotics and sedatives, other analgesics and antipyretics, urologicals, other mineral supplements, i.v. solution additives, other diagnostic agents Will the patient be readmitted to the hospital within two weeks? Answer 1 for yes, 0 for no. Answer with only the number. Answer:
0
0
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How each clinical prediction task is turned into supervised fine-tuning data for the LoRA experiments.
Given the patient information, predict the number of weeks of stay in hospital. Answer 1 if no more than one week, Answer 2 if more than one week but not more than two weeks, Answer 3 if more than two weeks. Answer with only the number
Patient information: Age: 78 Gender: male Conditions: Coronary atherosclerosis of native coronary artery, Chronic total occlusion of coronary artery, Malignant neoplasm of rectum, Personal history of antineoplastic chemotherapy, Person boarding or alighting a pedal cycle injured in collision with railway train or railway vehicle Procedures: Percutaneous transluminal coronary angioplasty [PTCA], Insertion of non-drug-eluting coronary artery stent(s), Left heart cardiac catheterization, Coronary arteriography using two catheters, Insertion of two vascular stents, Procedure on two vessels Using Drugs: other analgesics and antipyretics, lipid modifying agents, plain, antithrombotic agents, i.v. solution additives, antidepressants, potassium, viral vaccines, vasodilators used in cardiac diseases, belladonna and derivatives, plain Answer:
1
Given the patient information, predict the mortality of the patient. Answer 1 if the patient will die, answer 0 otherwise. Answer with only the number
Patient information: Age: 64 Gender: female Conditions: Osteoarthrosis, localized, not specified whether primary or secondary, lower leg, Other and unspecified angina pectoris, Unspecified essential hypertension, Mitral valve disorders, Psoriatic arthropathy Procedures: Total knee replacement Using Drugs: selective calcium channel blockers with mainly vascular effects, beta blocking agents, lipid modifying agents, plain, vitamin a and d, incl. combinations of the two, antihistamines for systemic use, other nutrients, macrolides, lincosamides and streptogramins, anticholinergic agents, drugs for peptic ulcer and gastro-oesophageal reflux disease (gord), drugs for constipation, opioids, antidepressants, other analgesics and antipyretics, i.v. solutions, antivaricose therapy, antiemetics and antinauseants, antiinflammatory and antirheumatic products, non-steroids, antacids, calcium, angiotensin ii receptor blockers (arbs), plain, antithrombotic agents Will the patient die because of the above situation? Answer:
0
Given the patient information, predict the readmission of the patient. Answer 1 if the patient will be readmitted to the hospital within two weeks, answer 0 otherwise. Answer with only the number
Patient information: Age: 91 Gender: female Conditions: Open wound of forehead, without mention of complication, Hyposmolality and/or hyponatremia, Open wound of knee, leg [except thigh], and ankle, without mention of complication, Contusion of thigh, Contusion of scapular region, Open wound of scalp, without mention of complication, Unspecified essential hypertension, Insomnia, unspecified, Unspecified deficiency anemia, Acquired absence of both cervix and uterus, Acquired absence of intestine (large) (small), Personal history of malignant melanoma of skin, Fall from other slipping, tripping, or stumbling Procedures: Closure of skin and subcutaneous tissue of other sites Using Drugs: selective calcium channel blockers with mainly vascular effects, vitamin a and d, incl. combinations of the two, antidepressants, other analgesics and antipyretics, beta blocking agents, drugs for constipation, antithrombotic agents, antacids, calcium, i.v. solution additives Will the patient be readmitted to the hospital within two weeks? Answer:
0
Evidence for insight ③. Even trained on a fraction of the data, traditional ML models stay stable and still beat every LLM. Training subsets are drawn by stratified sampling from the original training set, and validation and test sets are unchanged.
| Method | Length-of-Stay Prediction | Mortality Prediction | Readmission Prediction | |||
|---|---|---|---|---|---|---|
| Macro F1 (%) | AUROC (%) | F1 (%) | AUROC (%) | F1 (%) | AUROC (%) | |
| Majority | 23.37(23.37, 23.37) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) |
| Minority | 10.72(10.72, 10.72) | 50.00(50.00, 50.00) | 20.88(20.88, 20.88) | 50.00(50.00, 50.00) | 13.64(13.64, 13.64) | 50.00(50.00, 50.00) |
| Traditional ML Models with 40% of Original Training Set from MIMIC-III | ||||||
| XGBoost | 63.99(63.84, 64.14) | 91.53(91.45, 91.61) | 62.16(62.03, 62.29) | 94.67(94.63, 94.71) | 17.69(17.59, 17.79) | 64.23(64.04, 64.42) |
| Logistic Regression | 62.44(62.28, 62.60) | 90.61(90.47, 90.75) | 60.24(60.14, 60.34) | 92.91(92.88, 92.94) | 18.32(18.10, 18.54) | 66.06(65.68, 66.44) |
| Decision Tree | 56.14(55.99, 56.29) | 83.16(82.98, 83.34) | 48.41(48.20, 48.62) | 81.17(80.89, 81.45) | 14.95(14.86, 15.04) | 53.61(53.31, 53.91) |
| Random Forest | 61.06(60.96, 61.16) | 90.88(90.78, 90.98) | 53.11(52.96, 53.26) | 90.78(90.70, 90.86) | 18.34(18.09, 18.59) | 66.40(65.95, 66.85) |
| AdaBoost | 62.54(62.34, 62.74) | 89.38(89.09, 89.67) | 57.04(56.76, 57.32) | 90.94(90.78, 91.10) | 16.26(16.09, 16.43) | 59.77(59.27, 60.27) |
| SVM | 65.05(64.90, 65.20) | 91.71(91.63, 91.79) | 60.51(60.30, 60.72) | 94.04(93.99, 94.09) | 17.97(17.70, 18.24) | 66.84(66.48, 67.20) |
| Naive Bayes | 45.57(45.48, 45.66) | 62.48(62.28, 62.68) | 24.86(24.81, 24.91) | 59.10(59.02, 59.18) | 16.63(16.51, 16.75) | 58.05(57.81, 58.29) |
| KNN | 45.34(45.24, 45.44) | 74.43(74.28, 74.58) | 42.17(41.90, 42.44) | 83.75(83.59, 83.91) | 15.25(15.14, 15.36) | 59.80(59.38, 60.22) |
| MLP | 59.92(59.75, 60.09) | 87.99(87.81, 88.17) | 57.00(56.85, 57.15) | 90.77(90.70, 90.84) | 18.25(18.07, 18.43) | 65.71(65.38, 66.04) |
| Transformer | 58.73(58.45, 59.01) | 87.22(87.02, 87.42) | 56.05(55.81, 56.29) | 91.74(91.71, 91.77) | 18.21(18.04, 18.38) | 65.49(65.19, 65.79) |
| RNN | 61.07(60.88, 61.26) | 88.08(87.94, 88.22) | 58.77(58.60, 58.94) | 90.08(89.96, 90.20) | 19.02(18.86, 19.18) | 67.38(67.11, 67.65) |
| Traditional ML Models with 20% of Original Training Set from MIMIC-III | ||||||
| XGBoost | 61.95(61.81, 62.09) | 90.89(90.78, 91.00) | 57.37(57.07, 57.67) | 92.86(92.78, 92.94) | 16.55(16.35, 16.75) | 61.33(60.79, 61.87) |
| Logistic Regression | 60.78(60.55, 61.01) | 89.71(89.52, 89.90) | 56.55(56.24, 56.86) | 91.57(91.47, 91.67) | 16.69(16.45, 16.93) | 63.59(63.19, 63.99) |
| Decision Tree | 54.8(54.64, 54.96) | 79.85(79.65, 80.05) | 44.23(43.66, 44.80) | 77.20(76.99, 77.41) | 15.01(14.86, 15.16) | 56.25(55.94, 56.56) |
| Random Forest | 60.48(60.28, 60.68) | 90.59(90.48, 90.70) | 52.00(51.85, 52.15) | 90.15(90.05, 90.25) | 16.90(16.77, 17.03) | 63.09(62.93, 63.25) |
| AdaBoost | 60.51(60.32, 60.70) | 87.97(87.76, 88.18) | 53.76(53.60, 53.92) | 88.92(88.77, 89.07) | 15.75(15.50, 16.00) | 58.43(58.01, 58.85) |
| SVM | 63.05(62.92, 63.18) | 91.10(91.02, 91.18) | 57.35(57.01, 57.69) | 92.72(92.64, 92.80) | 17.41(17.20, 17.62) | 64.88(64.58, 65.18) |
| Naive Bayes | 43.82(43.77, 43.87) | 62.88(62.70, 63.06) | 25.63(25.57, 25.69) | 60.44(60.34, 60.54) | 16.71(16.54, 16.88) | 58.02(57.67, 58.37) |
| KNN | 44.49(44.33, 44.65) | 74.31(74.14, 74.48) | 42.39(42.21, 42.57) | 82.91(82.77, 83.05) | 15.04(14.94, 15.14) | 58.93(58.47, 59.39) |
| MLP | 58.66(58.41, 58.91) | 87.28(87.05, 87.51) | 53.54(53.24, 53.84) | 89.98(89.84, 90.12) | 17.08(16.80, 17.36) | 62.81(62.39, 63.23) |
| Transformer | 58.69(58.44, 58.94) | 88.18(87.96, 88.40) | 53.02(52.84, 53.20) | 90.74(90.56, 90.92) | 17.04(16.88, 17.20) | 61.73(61.37, 62.09) |
| RNN | 61.16(60.89, 61.43) | 88.74(88.51, 88.97) | 55.77(55.36, 56.18) | 89.13(88.89, 89.37) | 17.85(17.66, 18.04) | 64.03(63.78, 64.28) |
| Majority | 23.37(23.37, 23.37) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) | 0.00(0.00, 0.00) | 50.00(50.00, 50.00) |
| Minority | 10.72(10.72, 10.72) | 50.00(50.00, 50.00) | 20.88(20.88, 20.88) | 50.00(50.00, 50.00) | 13.64(13.64, 13.64) | 50.00(50.00, 50.00) |
| Traditional ML Models with 10% of Original Training Set from MIMIC-III | ||||||
| XGBoost | 61.35(61.18, 61.52) | 90.16(90.05, 90.27) | 51.62(51.37, 51.87) | 89.93(89.82, 90.04) | 17.05(16.81, 17.29) | 61.43(60.79, 62.07) |
| Logistic Regression | 60.79(60.64, 60.94) | 90.25(90.10, 90.40) | 53.92(53.64, 54.20) | 90.49(90.39, 90.59) | 17.71(17.54, 17.88) | 62.21(61.85, 62.57) |
| Decision Tree | 53.07(52.98, 53.16) | 77.11(76.77, 77.45) | 41.43(41.16, 41.70) | 74.65(74.43, 74.87) | 15.32(15.17, 15.47) | 55.75(55.36, 56.14) |
| Random Forest | 60.39(60.25, 60.53) | 90.61(90.54, 90.68) | 50.00(49.75, 50.25) | 88.72(88.63, 88.81) | 17.47(17.35, 17.59) | 63.51(63.32, 63.70) |
| AdaBoost | 60.3(60.23, 60.37) | 87.55(87.40, 87.70) | 50.07(49.63, 50.51) | 85.78(85.61, 85.95) | 16.03(15.80, 16.26) | 57.48(57.07, 57.89) |
| SVM | 62.33(62.2, 62.46) | 90.73(90.66, 90.8) | 52.51(52.17, 52.85) | 90.97(90.86, 91.08) | 17.01(16.81, 17.21) | 52.28(50.47, 54.09) |
| Naive Bayes | 41.54(41.39, 41.69) | 63.82(63.65, 63.99) | 26.48(26.32, 26.64) | 62.18(61.81, 62.55) | 14.87(14.79, 14.95) | 54.25(54.11, 54.39) |
| KNN | 42.37(42.23, 42.51) | 73.74(73.49, 73.99) | 41.26(41.13, 41.39) | 82.37(82.31, 82.43) | 14.30(14.23, 14.37) | 57.96(57.27, 58.65) |
| MLP | 58.65(58.39, 58.91) | 87.51(87.32, 87.70) | 51.55(51.17, 51.93) | 89.57(89.48, 89.66) | 15.95(15.83, 16.07) | 59.23(59.00, 59.46) |
| Transformer | 58.94(58.63, 59.25) | 89.43(89.25, 89.61) | 50.19(49.81, 50.57) | 89.43(89.31, 89.55) | 15.76(15.52, 16.00) | 58.34(57.91, 58.77) |
| RNN | 61.81(61.54, 62.08) | 89.20(89.04, 89.36) | 54.62(54.32, 54.92) | 89.00(88.88, 89.12) | 13.97(13.55, 14.39) | 61.82(61.65, 61.99) |
| Traditional ML Models with 5% of Original Training Set from MIMIC-III | ||||||
| XGBoost | 59.21(59.00, 59.42) | 89.16(89.00, 89.32) | 48.60(48.39, 48.81) | 87.67(87.58, 87.76) | 16.40(16.18, 16.62) | 58.90(58.49, 59.31) |
| Logistic Regression | 59.90(59.74, 60.06) | 89.92(89.74, 90.10) | 52.87(52.63, 53.11) | 89.50(89.36, 89.64) | 15.51(15.37, 15.65) | 58.96(58.50, 59.42) |
| Decision Tree | 52.35(52.17, 52.53) | 76.17(75.98, 76.36) | 39.02(38.44, 39.60) | 70.48(69.96, 71.00) | 15.90(15.77, 16.03) | 56.24(56.01, 56.47) |
| Random Forest | 60.89(60.74, 61.04) | 90.34(90.26, 90.42) | 52.10(51.83, 52.37) | 89.15(88.99, 89.31) | 15.75(15.58, 15.92) | 60.29(59.84, 60.74) |
| AdaBoost | 56.63(56.35, 56.91) | 86.61(86.43, 86.79) | 45.37(45.06, 45.68) | 83.13(82.77, 83.49) | 15.41(15.33, 15.49) | 59.09(58.89, 59.29) |
| SVM | 61.33(61.20, 61.46) | 90.10(90.00, 90.20) | 52.58(52.24, 52.92) | 90.04(89.93, 90.15) | 15.95(15.73, 16.17) | 49.55(47.98, 51.12) |
| Naive Bayes | 42.91(42.71, 43.11) | 66.12(65.90, 66.34) | 29.89(29.72, 30.06) | 66.44(66.25, 66.63) | 14.06(13.82, 14.30) | 53.30(53.02, 53.58) |
| KNN | 39.98(39.91, 40.05) | 71.55(71.39, 71.71) | 40.70(40.43, 40.97) | 83.04(82.93, 83.15) | 14.26(14.23, 14.29) | 55.74(55.13, 56.35) |
| MLP | 57.97(57.79, 58.15) | 87.22(87.03, 87.41) | 50.27(50.04, 50.50) | 87.92(87.77, 88.07) | 14.91(14.82, 15.00) | 57.36(57.01, 57.71) |
| Transformer | 58.23(57.95, 58.51) | 88.73(88.54, 88.92) | 48.67(48.51, 48.83) | 87.88(87.68, 88.08) | 14.04(13.72, 14.36) | 54.06(53.55, 54.57) |
| RNN | 60.94(60.74, 61.14) | 88.19(88.03, 88.35) | 52.84(52.61, 53.07) | 88.04(87.85, 88.23) | 11.92(11.21, 12.63) | 59.50(59.16, 59.84) |
| Majority | 29.56(29.56, 29.56) | 50.0(50.0, 50.0) | 0.0(0.0, 0.0) | 50.0(50.0, 50.0) | 0.0(0.0, 0.0) | 50.0(50.0, 50.0) |
| Minority | 4.56(4.56, 4.56) | 50.0(50.0, 50.0) | 6.7(6.7, 6.7) | 50.0(50.0, 50.0) | 24.92(24.92, 24.92) | 50.0(50.0, 50.0) |
| Traditional ML Models with 40% of Original Training Set from MIMIC-IV | ||||||
| XGBoost | 61.81(61.70, 61.92) | 94.51(94.46, 94.56) | 44.06(43.66, 44.46) | 97.36(97.31, 97.41) | 26.87(26.77, 26.97) | 58.86(58.69, 59.03) |
| Logistic Regression | 60.60(60.41, 60.79) | 93.64(93.58, 93.70) | 47.10(46.86, 47.34) | 97.63(97.59, 97.67) | 26.24(26.15, 26.33) | 58.01(57.84, 58.18) |
| Decision Tree | 53.07(52.76, 53.38) | 86.57(86.50, 86.64) | 30.97(30.42, 31.52) | 85.31(85.00, 85.62) | 25.33(25.05, 25.61) | 54.97(54.84, 55.10) |
| Random Forest | 57.86(57.78, 57.94) | 92.06(91.97, 92.15) | 34.24(33.77, 34.71) | 95.18(95.04, 95.32) | 27.08(27.00, 27.16) | 59.52(59.35, 59.69) |
| AdaBoost | 60.64(60.53, 60.75) | 92.50(92.45, 92.55) | 43.14(42.75, 43.53) | 95.29(95.11, 95.47) | 26.17(26.11, 26.23) | 56.45(56.29, 56.61) |
| SVM | 64.50(64.39, 64.61) | 95.48(95.42, 95.54) | 42.79(42.41, 43.17) | 97.51(97.47, 97.55) | 28.32(28.22, 28.42) | 60.85(60.70, 61.00) |
| Naive Bayes | 42.47(42.37, 42.57) | 70.00(69.84, 70.16) | 16.13(15.99, 16.27) | 77.47(77.36, 77.58) | 25.25(25.14, 25.36) | 52.79(52.58, 53.00) |
| KNN | 43.54(43.31, 43.77) | 71.62(71.35, 71.89) | 38.66(38.15, 39.17) | 90.02(89.83, 90.21) | 23.11(22.78, 23.44) | 53.63(53.35, 53.91) |
| MLP | 58.70(58.62, 58.78) | 90.83(90.75, 90.91) | 42.41(42.14, 42.68) | 97.17(97.14, 97.20) | 25.81(25.65, 25.97) | 56.94(56.76, 57.12) |
| Transformer | 57.66(57.44, 57.88) | 91.56(91.47, 91.65) | 40.94(40.48, 41.40) | 97.38(97.33, 97.43) | 25.33(25.27, 25.39) | 56.36(56.27, 56.45) |
| RNN | 61.63(61.53, 61.73) | 91.74(91.69, 91.79) | 46.56(46.37, 46.75) | 96.89(96.85, 96.93) | 25.56(25.47, 25.65) | 56.74(56.60, 56.88) |
| Traditional ML Models with 20% of Original Training Set from MIMIC-IV | ||||||
| XGBoost | 60.05(59.97, 60.13) | 93.49(93.41, 93.57) | 38.80(38.51, 39.09) | 96.21(96.15, 96.27) | 25.16(25.05, 25.27) | 56.43(56.21, 56.65) |
| Logistic Regression | 59.51(59.31, 59.71) | 92.87(92.71, 93.03) | 42.81(42.55, 43.07) | 96.91(96.84, 96.98) | 25.27(25.16, 25.38) | 56.00(55.89, 56.11) |
| Decision Tree | 52.15(51.83, 52.47) | 84.08(83.91, 84.25) | 27.27(26.49, 28.05) | 83.42(83.04, 83.80) | 21.39(20.78, 22.00) | 52.87(52.66, 53.08) |
| Random Forest | 57.70(57.55, 57.85) | 91.85(91.75, 91.95) | 34.77(34.30, 35.24) | 95.14(95.01, 95.27) | 26.55(26.46, 26.64) | 58.08(57.85, 58.31) |
| AdaBoost | 57.76(57.58, 57.94) | 91.66(91.58, 91.74) | 38.77(38.60, 38.94) | 94.49(94.27, 94.71) | 24.88(24.80, 24.96) | 54.71(54.62, 54.80) |
| SVM | 63.14(63.06, 63.22) | 94.88(94.82, 94.94) | 39.96(39.57, 40.35) | 96.53(96.46, 96.60) | 26.62(26.45, 26.79) | 58.14(57.93, 58.35) |
| Naive Bayes | 39.13(39.00, 39.26) | 70.63(70.41, 70.85) | 14.53(14.40, 14.66) | 75.68(75.44, 75.92) | 24.66(24.46, 24.86) | 53.69(53.48, 53.90) |
| KNN | 44.05(43.75, 44.35) | 72.61(72.43, 72.79) | 38.87(38.39, 39.35) | 87.61(87.30, 87.92) | 22.96(22.78, 23.14) | 53.41(53.23, 53.59) |
| MLP | 57.34(57.16, 57.52) | 89.89(89.76, 90.02) | 39.84(39.51, 40.17) | 96.59(96.53, 96.65) | 24.37(24.29, 24.45) | 54.71(54.58, 54.84) |
| Transformer | 57.39(57.19, 57.59) | 91.72(91.57, 91.87) | 35.91(35.18, 36.64) | 96.46(96.38, 96.54) | 24.35(24.24, 24.46) | 54.57(54.43, 54.71) |
| RNN | 60.27(60.07, 60.47) | 90.79(90.62, 90.96) | 42.13(41.87, 42.39) | 96.20(96.13, 96.27) | 24.77(24.67, 24.87) | 55.31(55.23, 55.39) |
| Majority | 29.56(29.56, 29.56) | 50.0(50.0, 50.0) | 0.0(0.0, 0.0) | 50.0(50.0, 50.0) | 0.0(0.0, 0.0) | 50.0(50.0, 50.0) |
| Minority | 4.56(4.56, 4.56) | 50.0(50.0, 50.0) | 6.7(6.7, 6.7) | 50.0(50.0, 50.0) | 24.92(24.92, 24.92) | 50.0(50.0, 50.0) |
| Traditional ML Models with 10% of Original Training Set from MIMIC-IV | ||||||
| XGBoost | 56.22(56.14, 56.30) | 91.79(91.69, 91.89) | 33.59(33.07, 34.11) | 94.67(94.53, 94.81) | 24.84(24.69, 24.99) | 54.70(54.42, 54.98) |
| Logistic Regression | 57.96(57.80, 58.12) | 92.13(92.00, 92.26) | 40.20(39.65, 40.75) | 96.25(96.18, 96.32) | 24.49(24.36, 24.62) | 54.82(54.58, 55.06) |
| Decision Tree | 49.48(49.12, 49.84) | 80.08(79.75, 80.41) | 26.82(26.23, 27.41) | 80.25(79.76, 80.74) | 24.23(23.78, 24.68) | 53.16(52.91, 53.41) |
| Random Forest | 57.15(56.97, 57.33) | 91.66(91.54, 91.78) | 35.29(34.67, 35.91) | 95.16(94.97, 95.35) | 25.42(25.20, 25.64) | 56.09(55.81, 56.37) |
| AdaBoost | 54.47(54.31, 54.63) | 90.96(90.80, 91.12) | 39.87(39.48, 40.26) | 95.36(95.29, 95.43) | 23.13(22.95, 23.31) | 52.50(52.33, 52.67) |
| SVM | 62.02(61.87, 62.17) | 94.09(94.01, 94.17) | 36.53(36.12, 36.94) | 95.80(95.66, 95.94) | 24.72(24.51, 24.93) | 50.87(50.10, 51.64) |
| Naive Bayes | 36.28(36.20, 36.36) | 71.22(71.14, 71.30) | 13.66(13.58, 13.74) | 75.26(75.19, 75.33) | 24.06(23.90, 24.22) | 53.51(53.39, 53.63) |
| KNN | 40.62(40.34, 40.90) | 70.57(70.17, 70.97) | 34.72(34.03, 35.41) | 85.06(84.81, 85.31) | 22.31(22.20, 22.42) | 52.84(52.62, 53.06) |
| MLP | 56.07(55.96, 56.18) | 88.97(88.87, 89.07) | 36.12(35.78, 36.46) | 95.75(95.71, 95.79) | 23.86(23.74, 23.98) | 53.71(53.54, 53.88) |
| Transformer | 55.37(55.24, 55.50) | 89.94(89.82, 90.06) | 30.94(30.54, 31.34) | 95.21(95.12, 95.30) | 24.18(24.05, 24.31) | 53.91(53.75, 54.07) |
| RNN | 59.04(58.94, 59.14) | 89.39(89.27, 89.51) | 39.13(38.77, 39.49) | 95.64(95.58, 95.70) | 24.37(24.26, 24.48) | 54.44(54.26, 54.62) |
| Traditional ML Models with 5% of Original Training Set from MIMIC-IV | ||||||
| XGBoost | 54.47(54.38, 54.56) | 89.99(89.76, 90.22) | 25.04(24.52, 25.56) | 90.50(90.33, 90.67) | 24.39(24.25, 24.53) | 53.80(53.53, 54.07) |
| Logistic Regression | 57.20(57.03, 57.37) | 91.51(91.35, 91.67) | 34.45(33.87, 35.03) | 94.75(94.62, 94.88) | 23.96(23.78, 24.14) | 53.57(53.22, 53.92) |
| Decision Tree | 49.15(48.75, 49.55) | 75.08(73.98, 76.18) | 22.11(21.63, 22.59) | 78.31(77.88, 78.74) | 22.53(21.91, 23.15) | 52.28(52.01, 52.55) |
| Random Forest | 57.40(57.28, 57.52) | 91.53(91.35, 91.71) | 34.35(33.68, 35.02) | 94.07(93.88, 94.26) | 24.94(24.76, 25.12) | 54.40(53.97, 54.83) |
| AdaBoost | 50.14(49.93, 50.35) | 90.59(90.47, 90.71) | 37.37(36.54, 38.20) | 92.59(92.34, 92.84) | 23.44(23.28, 23.60) | 52.27(51.99, 52.55) |
| SVM | 60.63(60.36, 60.90) | 93.00(92.86, 93.14) | 32.92(32.52, 33.32) | 93.75(93.61, 93.89) | 24.75(24.52, 24.98) | 50.00(49.23, 50.77) |
| Naive Bayes | 34.42(34.32, 34.52) | 70.08(69.74, 70.42) | 12.74(12.58, 12.90) | 73.99(73.69, 74.29) | 23.14(23.04, 23.24) | 52.19(52.07, 52.31) |
| KNN | 38.72(38.33, 39.11) | 67.40(67.07, 67.73) | 24.09(23.40, 24.78) | 79.08(78.66, 79.50) | 22.13(21.89, 22.37) | 52.32(52.13, 52.51) |
| MLP | 54.25(54.10, 54.40) | 86.36(86.17, 86.55) | 32.24(31.75, 32.73) | 94.16(94.05, 94.27) | 23.68(23.61, 23.75) | 53.13(52.86, 53.40) |
| Transformer | 54.50(54.35, 54.65) | 88.37(88.09, 88.65) | 22.59(22.04, 23.14) | 91.43(91.19, 91.67) | 23.99(23.87, 24.11) | 52.83(52.58, 53.08) |
| RNN | 57.88(57.70, 58.06) | 87.51(87.14, 87.88) | 17.32(16.98, 17.66) | 92.38(92.25, 92.51) | 24.09(23.96, 24.22) | 53.77(53.48, 54.06) |
Average over five runs. Brackets give the 95% confidence interval. Majority / Minority are repeated within each block for reference.
Each grid shows all 11 traditional ML models, then 9 general-purpose LLMs, then 6 medical LLMs, in the order used in the main results table.
Evaluation loss over training steps, showing that the LLMs mostly converge within the 20 training epochs used in the fine-tuning experiments. The best checkpoint is selected on the validation set.
do_sample=False) for reproducibility.@inproceedings{chen2026clinicalbench,
title = {ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?},
author = {Chen, Canyu and Yu, Jian and Chen, Shan and Liu, Che and Wan, Zhongwei
and Zhou, Shuang and Luo, Yuan and Zhang, Rui and Bitterman, Danielle S.
and Wang, Fei and Shu, Kai},
booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery
and Data Mining (KDD '26)},
year = {2026}
}