用 (大) 语言模型进行临床结果预测的不确定性量化.
Zizhang Chen1, Peizhao Li2, Xiaomeng Dong2
1Brandeis University.
概括
这项研究通过量化电子健康记录 (EHR) 语言模型 (LMs) 的不确定性来提高医疗保健中的AI可靠性. 组合和多任务等方法减少预测不确定性,提高AI透明度和患者安全.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 机器学习用于医疗保健
背景情况:
- 语言模型 (LMs) 显示出使用电子健康记录 (EHRs) 的临床预测的前景.
- 高风险的医疗保健应用要求可靠的AI预测,需要强大的不确定性量化.
- 当前的人工智能模型往往缺乏透明度,对患者安全和道德标准构成风险.
研究的目的:
- 开发和验证一个框架,用于在EHR任务中量化LM的不确定性.
- 为了解决白盒 (可访问参数) 和黑盒 (专有LM,如GPT-4) 设置中的不确定性.
- 提高人工智能驱动的临床预测的可靠性和透明度.
主要方法:
- 在使用多任务和组合技术的白盒LM中量化不确定性.
- 扩展不确定性量化到黑子模型,包括专有LM.
- 在10个预测任务中验证了来自6000多名患者的纵向临床数据的框架.
主要成果:
- 建议的多任务和组合方法有效地减少了EHR任务中的模型不确定性.
- 组合和多任务预测提示显示,在各种临床预测场景中,不确定性减少.
- 该框架在白盒和黑盒设置中成功提高了模型透明度.
结论:
- 使用组合和多任务的不确定性量化提高了EMS的LM的可靠性.
- 开发的框架提高了AI在临床决策支持中的透明度和可信度.
- 这项工作促进了人工智能在医疗保健提供中的安全和道德整合.
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