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

翻译：我们提出了一种基于大语言模型的临床预测方法（Clinical Prediction with Large Language Models，简称CPLLM），该方法通过对预训练大语言模型进行微调，实现临床疾病诊断与再入院预测。我们采用量化技术并基于提示对LLM进行微调。在诊断预测任务中，我们利用患者的历史诊断记录，预测其在下次就诊或后续诊断中是否会被诊断为某种目标疾病。我们将结果与包括RETAIN和Med-BERT在内的多种基线模型进行对比，其中Med-BERT是利用时间结构化电子健康记录数据进行疾病预测的当前最优模型。此外，我们还评估了CPLLM在患者住院再入院预测中的表现，并将其性能与基准基线模型进行了比较。实验结果表明，本文提出的CPLLM方法在PR-AUC和ROC-AUC指标上均超越了所有测试模型，在诊断预测与患者住院再入院预测方面展现出最优结果。该方法易于实施且可无缝集成至临床流程中，有助于医护人员评估患者的后续治疗步骤。