canyuchen/ClinicalBench
Code for the paper "ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?"
This project offers a benchmark for clinicians and healthcare researchers to evaluate how well different large language models (LLMs) perform compared to traditional machine learning methods for crucial clinical predictions. You can input de-identified patient data from Electronic Health Records (EHR) to predict outcomes like hospital length-of-stay, mortality, or readmission. It's designed for medical professionals, data scientists in healthcare, or researchers who need to make informed decisions about adopting AI for clinical tasks.
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Use this if you need to compare the predictive accuracy of LLMs versus established machine learning models for clinical outcomes using real-world patient data.
Not ideal if you are looking for a tool to develop new LLMs or traditional ML models for clinical prediction, rather than benchmark existing ones.
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Language
Python
License
MIT
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Last pushed
Jun 18, 2025
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