  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.
