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An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur

Author

Listed:
  • Odai Y. Dweekat

    (Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Sarah S. Lam

    (Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA)

  • Lindsay McGrath

    (Wound Ostomy Continence Nursing, ChristianaCare Health System, Newark, DE 19718, USA)

Abstract

Hospital-Acquired Pressure Injury (HAPI), known as bedsore or decubitus ulcer, is one of the most common health conditions in the United States. Machine learning has been used to predict HAPI. This is insufficient information for the clinical team because knowing who would develop HAPI in the future does not help differentiate the severity of those predicted cases. This research develops an integrated system of multifaceted machine learning models to predict if and when HAPI occurs. Phase 1 integrates Genetic Algorithm with Cost-Sensitive Support Vector Machine (GA-CS-SVM) to handle the high imbalance HAPI dataset to predict if patients will develop HAPI. Phase 2 adopts Grid Search with SVM (GS-SVM) to predict when HAPI will occur for at-risk patients. This helps to prioritize who is at the highest risk and when that risk will be highest. The performance of the developed models is compared with state-of-the-art models in the literature. GA-CS-SVM achieved the best Area Under the Curve (AUC) (75.79 ± 0.58) and G-mean (75.73 ± 0.59), while GS-SVM achieved the best AUC (75.06) and G-mean (75.06). The research outcomes will help prioritize at-risk patients, allocate targeted resources and aid with better medical staff planning to provide intervention to those patients.

Suggested Citation

  • Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(1), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:828-:d:1022511
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    References listed on IDEAS

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    1. Ling Gao & Lina Yang & Xiaoqin Li & Jin Chen & Juan Du & Xiaoxia Bai & Xianjun Yang, 2018. "The use of a logistic regression model to develop a risk assessment of intraoperatively acquired pressure ulcer," Journal of Clinical Nursing, John Wiley & Sons, vol. 27(15-16), pages 2984-2992, August.
    2. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
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    1. Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur," IJERPH, MDPI, vol. 20(6), pages 1-18, March.

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