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Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review

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

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients’ Electronic Health Records (EHR).

Suggested Citation

  • Odai Y. Dweekat & Sarah S. Lam & Lindsay McGrath, 2023. "Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review," IJERPH, MDPI, vol. 20(1), pages 1-46, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:796-:d:1021961
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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. Che Wei Chang & Mesakh Christian & Dun Hao Chang & Feipei Lai & Tom J Liu & Yo Shen Chen & Wei Jen Chen, 2022. "Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-19, February.
    3. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    4. Dan Li & Carol Mathews, 2017. "Automated measurement of pressure injury through image processing," Journal of Clinical Nursing, John Wiley & Sons, vol. 26(21-22), pages 3564-3575, November.
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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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