EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
Evaluating gpt-4 and chatgpt on japanese medical licensing examinations
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Tag-based few-shot selection yields higher precision and stability than random or similarity-based methods when using LLMs to analyze medical incidents.
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EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning
EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
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Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning
Tag-based few-shot selection yields higher precision and stability than random or similarity-based methods when using LLMs to analyze medical incidents.