CPLLM: Clinical Prediction with Large Language Models
This work addresses clinical prediction tasks for healthcare providers, offering an incremental improvement over existing methods.
The authors tackled clinical disease and readmission prediction by fine-tuning a pre-trained Large Language Model (LLM) with quantization and prompts, achieving state-of-the-art results in PR-AUC and ROC-AUC metrics compared to baselines like RETAIN and Med-BERT.
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