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An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations

Author

Listed:
  • Abdulaziz Ahmed

    (The University of Alabama at Birmingham)

  • Mohammed Al-Maamari

    (University of Passau)

  • Mohammad Firouz

    (The University of Alabama at Birmingham)

  • Dursun Delen

    (Oklahoma State University
    Istinye University)

Abstract

Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. In this paper, the metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, and ASA-CaB. Grid search (GS), a traditional approach used for machine learning fine-tuning, is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The optimized model is used to develop an e-triage tool that can be used at EDs to predict ED patients' emergency severity index (ESI). The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, and 83.2%, respectively.

Suggested Citation

  • Abdulaziz Ahmed & Mohammed Al-Maamari & Mohammad Firouz & Dursun Delen, 2024. "An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations," Information Systems Frontiers, Springer, vol. 26(5), pages 1893-1913, October.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:5:d:10.1007_s10796-023-10431-4
    DOI: 10.1007/s10796-023-10431-4
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    References listed on IDEAS

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    1. Dowsland, Kathryn A., 1993. "Some experiments with simulated annealing techniques for packing problems," European Journal of Operational Research, Elsevier, vol. 68(3), pages 389-399, August.
    2. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
    3. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 2020. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 22(5), pages 1133-1161, October.
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