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A significant proportion of clinical physiologic monitoring alarms are false.This often leads to alarm fatigue in clinical personnel, inevitablycompromising patient safety. To combat this issue, researchers have attemptedto build Machine Learning (ML) models capable of accurately adjudicating VitalSign (VS) alerts raised at the bedside of hemodynamically monitored patients asreal or artifact. Previous studies have utilized supervised ML techniques thatrequire substantial amounts of hand-labeled data. However, manually harvestingsuch data can be costly, time-consuming, and mundane, and is a key factorlimiting the widespread adoption of ML in healthcare (HC). Instead, we explorethe use of multiple, individually imperfect heuristics to automatically assignprobabilistic labels to unlabeled training data using weak supervision. Ourweakly supervised models perform competitively with traditional supervisedtechniques and require less involvement from domain experts, demonstratingtheir use as efficient and practical alternatives to supervised learning in HCapplications of ML.

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact