A study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework

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dc.authorscopusidDursun Delen / 55887961100
dc.authorwosidDursun Delen / AGA-9892-2022
dc.contributor.authorAhmed, Abdulaziz
dc.contributor.authorAram, Khalid Y.
dc.contributor.authorTutun, Salih
dc.contributor.authorDelen, Dursun
dc.date.accessioned2025-04-18T08:47:30Z
dc.date.available2025-04-18T08:47:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe issue of left against medical advice (LAMA) patients is common in today’s emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to “leave against medical advice” is vital to mitigate and potentially eliminate these adverse outcomes. This paper proposes a framework for studying the factors that affect LAMA in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization-one of the main challenges of machine learning model development. Adaptive tabu simulated annealing (ATSA) metaheuristic algorithm is utilized for optimizing the parameters of extreme gradient boosting (XGB). The optimized XGB models are used to predict the LAMA outcomes for patients under treatment in ED. The designed algorithms are trained and tested using four data groups which are created using feature selection. The model with the best predictive performance is then interpreted using the SHaply Additive exPlanations (SHAP) method. The results show that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively. The best model was explained using SHAP method. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.
dc.identifier.citationAhmed, A., Aram, K. Y., Tutun, S., & Delen, D. (2024). A study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework. Health Care Management Science, 1-18.
dc.identifier.doi10.1007/s10729-024-09684-5
dc.identifier.endpage502
dc.identifier.issn13869620
dc.identifier.issue4
dc.identifier.pmid39138745
dc.identifier.scopus2-s2.0-85201255560
dc.identifier.scopusqualityQ1
dc.identifier.startpage485
dc.identifier.urihttp://dx.doi.org/10.1007/s10729-024-09684-5
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6614
dc.identifier.volume27
dc.identifier.wosWOS:001290057600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.institutionauthorDelen, Dursun
dc.institutionauthoridDursun Delen / 0000-0001-8857-5148
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofHealth Care Management Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEmergency Department
dc.subjectExplainable AI
dc.subjectLeft Against Medical Advice (LAMA)
dc.subjectMachine Learning
dc.subjectPredictive analytics
dc.subjectSimulated Annealing
dc.titleA study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework
dc.typeArticle

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