IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v27y2024i4d10.1007_s10729-024-09684-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-024-09684-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10729-024-09684-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:27:y:2024:i:4:d:10.1007_s10729-024-09684-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.