Interpretable Machine Learning Model for Predicting Laboratory Alert Fatigue Using Alert Frequency, Clinical Severity, Provider Specialty, Repeated Abnormal Results, Response Time, and Override Behavior

cirpublications.com-clJP-1790880671-e176116242 1790880671 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2024 4 1 Interpretable Machine Learning Model for Predicting Laboratory Alert Fatigue Using Alert Frequency, Clinical Severity, Provider Specialty, Repeated Abnormal Results, Response Time, and Override Behavior Chen Hao Liu Fang Zhao Lin 2024 10.68159/e176116242 https://cirpublications.com/pub/journal/2/article/e176116242 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R, et al. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak. 2017;17(1):36. Wong A, Amato MG, Seger DL, Slight SP, Beeler PE, Dykes PC, et al. Evaluation of medication-related clinical decision support alert overrides in the intensive care unit. J Crit Care. 2017;39:156-61. 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