对病人的具体病因的反事实性表述
1Department of Psychiatry & Behavioral Sciences, 1601 23rd Avenue South, Nashville, 37232, TN, United States of America.
Journal of biomedical informatics
|January 8, 2024
概括
这项研究使用反事实推理定义了患者特异性疾病的根本原因,使人工智能能够从数据中识别疾病起源. 这种方法与临床直觉一致,并提供可计算的根因果贡献得分.
科学领域:
- 医疗信息学 医疗信息学
- 因果推理因果推理
- 人工智能的人工智能
背景情况:
- 确定疾病的根本原因对于临床决策至关重要,但缺乏用于计算分析的严格数学框架.
- 目前医学中根源原因分析的方法通常依赖于医生的直观推理,很难将其转化为自动化算法.
研究的目的:
- 开发一种数学上严格的,符合临床直觉的,针对患者的疾病根源的定义.
- 通过使用计算方法从患者数据中自动检测疾病的根本原因.
主要方法:
- 使用结构方程模型和干预反事实.
- 运用了反事实回溯的数学形式化,用于根源原因的新定义.
- 从可解释的人工智能应用Shapley值来量化根源因果贡献.
主要成果:
- 引入了针对患者的疾病根源的反事实定义,与临床直觉和珍珠的因果关系梯子保持一致.
- 开发了一种方法,为每个变量分配一个根因果贡献得分.
- 证明该配方考虑了噪音标签,并适应了疾病流行率.
结论:
- 拟议的反事实表述提供了一种强大且计算效率高的方法,用于识别患者特异性疾病的根本原因.
- 这种方法弥合了临床直觉和用于根源原因检测的计算分析之间的差距.
- 该方法允许快速计算而不需要反事实模拟,从而促进实际应用.
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