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

Author

Listed:
  • Abdulaziz Ahmed

    (The University of Alabama at Birmingham
    University of Alabama at Birmingham)

  • Khalid Y. Aram

    (Emporia State University)

  • Salih Tutun

    (Washington University in St. Louis)

  • Dursun Delen

    (Oklahoma State University
    Istinye University)

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.

Suggested Citation

  • Abdulaziz Ahmed & Khalid Y. Aram & Salih Tutun & Dursun Delen, 2024. "A study of “left against medical advice” emergency department patients: an optimized explainable artificial intelligence framework," Health Care Management Science, Springer, vol. 27(4), pages 485-502, December.
    • Handle: RePEc:kap:hcarem:v:27:y:2024:i:4:d:10.1007_s10729-024-09684-5
    DOI: 10.1007/s10729-024-09684-5
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    References listed on IDEAS

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