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Title: Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.  more » « less
Award ID(s):
1950811
PAR ID:
10441685
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
AMIA Annual Symposium proceedings
ISSN:
1942-597X
Page Range / eLocation ID:
405-414
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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