CPLLM: Clinical Prediction with Large Language Models
Ofir Ben Shoham Nadav Rappoport
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.