CPLLM:使用大型语言模型进行临床预测
Ofir Ben Shoham1, Nadav Rappoport1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel.
PLOS digital health
|December 6, 2024
概括
使用大型语言模型 (CPLLM) 的临床预测准确地预测疾病诊断和医院再接收. 这种新的方法在不需要先前的医疗数据培训的情况下优于现有模型.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 医学数据科学 医学数据科学
背景情况:
- 预测临床疾病和再入院对于患者护理和医疗管理至关重要.
- 分析电子健康记录 (EHR) 的现有方法通常需要专门的预训练或与时间数据作斗争.
- 大型语言模型 (LLM) 在处理复杂数据方面表现有前途,但需要适应临床预测任务.
研究的目的:
- 引入用大型语言模型 (CPLLM) 进行临床预测,这是一种用于疾病诊断和医院再接收预测的新方法.
- 根据已建立的基线模型评估CPLLM的表现,包括最先进的Med-BERT.
- 为了证明CPLLM的有效性,而不需要在特定的医疗数据集上进行预先培训.
主要方法:
- 使用量化和基于提示的技术微调预训练的大型语言模型 (LLM).
- 利用患者历史病历进行诊断预测 (下一次访问或后续诊断).
- 评估CPLLM用于医院再接收预测,并与基准模型进行比较.
主要成果:
- 与所有测试的基线模型相比,CPLLM实现了更高的性能.
- 该方法在疾病预测和医院再入院预测方面都取得了最先进的结果,通过PR-AUC和ROC-AUC指标测量.
- 在不需要对医疗数据进行预先培训的情况下,CPLLM成功预测了临床结果.
结论:
- CPLLM为临床预测任务提供了一种强大而有效的方法,包括疾病诊断和医院再接收.
- 该方法在没有医疗预训练的情况下执行的能力简化了其实施和集成到临床工作流程中.
- CPLLM有可能在计划患者护理和干预方面显著帮助医疗保健提供者.
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