  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 and readmission prediction. We utilized quantization and fine-tuned the
LLM using prompts. For diagnosis prediction, we predict 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 to various baselines, including RETAIN, and Med-BERT, the current
state-of-the-art model for disease prediction using temporal structured EHR
data. In addition, We also evaluated CPLLM for patient hospital readmission
prediction and compared our method's performance with benchmark baselines. Our
experiments have shown that our proposed method, CPLLM, surpasses all the
tested models in terms of PR-AUC and ROC-AUC metrics, showing state-of-the-art
results for diagnosis prediction and patient hospital readmission prediction.
Such a method can be easily implemented and integrated into the clinical
process to help care providers estimate the next steps of patients
