用大模型分析病历数据，零样本预测药物过量风险
Large Language Models for Drug Overdose Prediction from Longitudinal Medical Records
- 用GPT-4o处理长期医疗记录文本，无需训练即可识别风险
- 零样本下准确率超越传统机器学习模型
- 适合临床预警系统开发与医疗AI研究者
从患者长期医疗记录中预测药物过量风险对及时干预至关重要。传统机器学习模型已在该任务中展现潜力。近期大型语言模型（LLMs）的发展为提升预测性能提供了新可能，因其能处理长文本并具备跨任务先验知识。本研究评估了Open AI的GPT-4o在利用患者长期保险理赔记录预测药物过量事件中的有效性。我们在微调与零样本两种设置下评估其表现，并与强基准的传统机器学习方法对比。结果表明，大模型在某些场景下不仅优于传统模型，还能在无任务特定训练的情况下实现过量风险预测。这些发现凸显了大模型在临床决策支持中的潜力，尤其在药物过量风险预测方面。
原文摘要 · Abstract (English)
The ability to predict drug overdose risk from a patient's medical records is crucial for timely intervention and prevention. Traditional machine learning models have shown promise in analyzing longitudinal medical records for this task. However, recent advancements in large language models (LLMs) offer an opportunity to enhance prediction performance by leveraging their ability to process long textual data and their inherent prior knowledge across diverse tasks. In this study, we assess the effectiveness of Open AI's GPT-4o LLM in predicting drug overdose events using patients' longitudinal insurance claims records. We evaluate its performance in both fine-tuned and zero-shot settings, comparing them to strong traditional machine learning methods as baselines. Our results show that LLMs not only outperform traditional models in certain settings but can also predict overdose risk in a zero-shot setting without task-specific training. These findings highlight the potential of LLMs in clinical decision support, particularly for drug overdose risk prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。