临床机器学习中，医生更信任能原生处理缺失值的模型。
Handling missing values in clinical machine learning: Insights from an expert study
- 医生倾向用现有数据和经验判断，而非补全缺失值
- 55位医生中20人参与实验，验证了原生处理缺失值的模型更受青睐
- 研究强调将临床推理融入模型设计，提升医工协作效率
可解释机器学习（IML）模型在临床决策中具有重要价值，但面对缺失特征时面临挑战。传统方法如插补或丢弃不完整记录，在测试阶段数据缺失时往往不可行。我们对来自29家法国创伤中心的55名临床医生进行了调查，收集到20份有效反馈，评估他们在真实临床场景中使用三种IML模型预测出血性休克时对缺失值的应对方式。结果表明，尽管医生认可可解释性并熟悉常见IML方法，但传统插补手段常与他们的临床直觉冲突。他们更倾向于依赖已观察特征，结合医学经验和判断。因此，能原生处理缺失值的模型更受青睐。该研究凸显了未来IML模型需融合临床推理以增强人机交互效果。
原文摘要 · Abstract (English)
Inherently interpretable machine learning (IML) models offer valuable support for clinical decision-making but face challenges when features contain missing values. Traditional approaches, such as imputation or discarding incomplete records, are often impractical in scenarios where data is missing at test time. We surveyed 55 clinicians from 29 French trauma centers, collecting 20 complete responses to study their interaction with three IML models in a real-world clinical setting for predicting hemorrhagic shock with missing values. Our findings reveal that while clinicians recognize the value of interpretability and are familiar with common IML approaches, traditional imputation techniques often conflict with their intuition. Instead of imputing unobserved values, they rely on observed features combined with medical intuition and experience. As a result, methods that natively handle missing values are preferred. These findings underscore the need to integrate clinical reasoning into future IML models to enhance human-computer interaction.
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