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

Abdulaziz Ahmed (), Khalid Y. Aram, Salih Tutun and Dursun Delen
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Abdulaziz Ahmed: The University of Alabama at Birmingham
Khalid Y. Aram: Emporia State University
Salih Tutun: Washington University in St. Louis
Dursun Delen: Oklahoma State University

Health Care Management Science, 2024, vol. 27, issue 4, No 1, 485-502

Abstract: Abstract The 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.

Keywords: Left against medical advice (LAMA); Predictive analytics; Machine learning; Simulated annealing; Emergency department; Explainable AI (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10729-024-09684-5

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