大型语言模型促进电子健康记录生成 现型化算法
Chao Yan1, Henry H Ong1, Monika E Grabowska1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
medRxiv : the preprint server for health sciences
|January 10, 2024
概括
像GPT-4这样的大型语言模型可以起草电子健康记录表型算法. 然而,仍需要专家审查来完善这些算法以获得临床准确性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 观察性健康研究 观察性健康研究
背景情况:
- 电子健康记录 (EHR) 对观察性健康研究至关重要,但表型化需要广泛的专家投入,限制了可扩展性.
- 开发准确的表型算法是耗时的,涉及文献审查和证据合成,这阻碍了快速的知识发现.
结论:
- 大型语言模型 (LLM) 在加速EHR表型化算法的初始起草方面表现有前途.
- 当前的LLM生成算法需要实质性的专家监督验证和改进,突出显示了对临床信息学专业知识的持续需求.
- 法律法规可以提高EHR表型化的效率,但人类专业知识仍然对于确保最终算法的质量和可靠性至关重要.
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