医生参与改进机器学习识别谵妄，提升准确性和可靠性。
Can Physician Expertise Improve Machine Learning Identification of Delirium?
- 医生指导特征优化与模型评估，结合可解释性分析。
- 在3862例住院数据上表现优于自动模型，时间鲁棒性强。
- 适合临床研究者和医疗AI开发者参考应用。
谵妄在住院患者中常见，但常被忽视。本文提出一种以用户为中心的交互式机器学习（UC-iML）框架，用于支持谵妄检测，结合医生引导的特征精炼与可解释建模。基于多伦多六家医院的通用医学住院患者计划（GEMINI）中3,862例标注住院记录，整合了行政变量、检验结果、药物信息及放射学文本指标。医生参与特征优化与模型评估，采用SHAP（Shapley Additive exPlanations）进行特征重要性分析。通过时间分离的测试集与后期验证队列评估标准监督分类器。相比自动化与基线模型，该框架表现出更优的整体区分能力与更强的时间稳定性，且解释结果揭示了临床有意义的信号。结果支持UC-iML作为临床相关谵妄建模的实际人机协同框架。
原文摘要 · Abstract (English)
Delirium is common in hospitalized patients and is often missed in routine care. We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support that combines physician-guided feature refinement with interpretable modeling. Using 3,862 labeled admissions from six Toronto hospitals in the General Medicine Inpatient Initiative (GEMINI), we integrate administrative variables, laboratory results, medications, and a radiology-derived text indicator. Physicians guide feature refinement and model evaluation, and Shapley Additive exPlanations (SHAP) are used to summarize feature attribution. We evaluate standard supervised classifiers with temporally separated holdout testing and a later-phase validation cohort. Compared with automated and baseline variants, the proposed framework shows better overall discrimination and stronger temporal robustness, while the explanations highlight clinically meaningful signals. These results support UC-iML as a practical human-in-the-loop framework for clinically relevant delirium modeling.
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