CPLLM: Clinical Prediction with Large Language Models

Ofir Ben Shoham, Nadav Rappoport

Keywords: NLP, EHR, LLM, classification

TL;DR: CPLLM: a new state-of-the-art method for disease prediction in structured EHR data using fine-tuning LLM

Abstract: We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for clinical disease prediction. We utilized quantization and fine-tuned the LLM using prompts, with the task of predicting whether patients will be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical diagnosis records. We compared our results versus various baselines, including Logistic Regression, RETAIN, and Med-BERT, which is the current state-of-the-art model for disease prediction using structured EHR data. Our experiments have shown that CPLLM surpasses all the tested models in terms of both PR-AUC and ROC-AUC metrics, displaying noteworthy enhancements compared to the baseline models

Primary Area: generative models

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Submission Number: 1994