Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit

Danielle Jeddah; Ofer Chen; Ari M. Lipsky; Andrea Forgacs; Gershon Celniker; Craig M. Lilly; Itai M. Pessach

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자료유형: 학술저널

저자정보: Danielle Jeddah (Tel-Aviv University) Ofer Chen (Clew Medical Ltd) Ari M. Lipsky (lew Medical Ltd.) Andrea Forgacs (Clew Medical Ltd.) Gershon Celniker (Clew Medical Ltd.) Craig M. Lilly (University of Massachusetts Medical School) Itai M. Pessach (Tel-Aviv University)

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제27권 제3호

발행연도: 2021.1

수록면: 241 - 248 (8page)

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Objectives: Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers. Methods: This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system. Results: The automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers. Conclusions: We demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.

#Critical Care #Big Data #Respiratory Insufficiency #Clinical Deterioration #Artificial Intelligence

참고문헌 (14)

참고문헌 신청

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