VBridge：连接功能和数据之间的点来解释医疗保健模型
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
摘要：
机器学习（ML）越来越多地应用于电子健康记录（EHR）以解决临床预测任务。尽管许多ML模型表现良好，但模型透明度和可解释性的问题限制了它们在临床实践中的采用。在临床环境中直接使用现有的可解释ML技术可能具有挑战性。通过文献调查和与6名平均拥有17年临床经验的临床医生的合作，我们确定了三个关键挑战，包括临床医生对ML功能的不熟悉，缺乏上下文信息以及对队列级别证据的需求。在迭代设计过程之后，我们进一步设计和开发了VBridge，这是一种可视化分析工具，可以将ML解释无缝集成到临床医生的决策工作流程中。该系统包括一个新颖的基于贡献的特征解释的分层显示和丰富的交互，将ML特征，解释和数据之间的点连接起来。我们通过两个案例研究和对四位临床医生的专家访谈证明了VBridge的有效性，表明将模型解释与患者的情景记录可视化关联可以帮助临床医生在做出临床决策时更好地解释和使用模型预测。我们进一步得出了一系列设计意义，用于开发未来可解释的ML工具以支持临床决策。
英文摘要：
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model transparency and interpretability limit their adoption in clinical practice. Directly using existing explainable ML techniques in clinical settings can be challenging. Through literature surveys and collaborations with six clinicians with an average of 17 years of clinical experience, we identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence. Following an iterative design process, we further designed and developed VBridge, a visual analytics tool that seamlessly incorporates ML explanations into clinicians' decision-making workflow. The system includes a novel hierarchical display of contribution-based feature explanations and enriched interactions that connect the dots between ML features, explanations, and data. We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians, showing that visually associating model explanations with patients' situational records can help clinicians better interpret and use model predictions when making clinician decisions. We further derived a list of design implications for developing future explainable ML tools to support clinical decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
