This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (83) Artificial Intelligence (1198) Machine Learning (1385) Pressure Ulcers and Decubitus Wounds (25)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25704, first published .
Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Authors of this article:

Mengyao Jiang1 Author Orcid Image ;   Yuxia Ma1 Author Orcid Image ;   Siyi Guo2 Author Orcid Image ;   Liuqi Jin2 Author Orcid Image ;   Lin Lv3 Author Orcid Image ;   Lin Han1, 4 Author Orcid Image ;   Ning An2 Author Orcid Image
Article Authors Cited by (33) Tweetations (3) Metrics

Journals

  1. Falcone M, De Angelis B, Pea F, Scalise A, Stefani S, Tasinato R, Zanetti O, Dalla Paola L. Challenges in the management of chronic wound infections. Journal of Global Antimicrobial Resistance 2021;26:140 View
  2. Dweekat O, Lam S, McGrath L. Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. International Journal of Environmental Research and Public Health 2023;20(1):796 View
  3. Eshetie T, Moldovan M, Caughey G, Lang C, Sluggett J, Khadka J, Whitehead C, Crotty M, Corlis M, Visvanathan R, Wesselingh S, Inacio M. Development of a Multivariable Prediction Model for Risk of Hospitalization With Pressure Injury After Entering Residential Aged Care. Journal of the American Medical Directors Association 2023;24(3):299 View
  4. Hu X, Fang H, Wang P. Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?. Tropical Medicine and Infectious Disease 2022;7(8):184 View
  5. Silva A, Metrôlho J, Ribeiro F, Fidalgo F, Santos O, Dionisio R. A Review of Intelligent Sensor-Based Systems for Pressure Ulcer Prevention. Computers 2021;11(1):6 View
  6. Zhou Y, Yang X, Ma S, Yuan Y, Yan M. A systematic review of predictive models for hospital‐acquired pressure injury using machine learning. Nursing Open 2023;10(3):1234 View
  7. Dweekat O, Lam S, McGrath L. An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur. International Journal of Environmental Research and Public Health 2023;20(6):4911 View
  8. Ribeiro F, Fidalgo F, Silva A, Metrôlho J, Santos O, Dionisio R. Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities. Informatics 2021;8(4):76 View
  9. Kang Y, Topaz M, Dunbar S, Stehlik J, Hurdle J. The Utility of Nursing Notes Among Medicare Patients With Heart Failure to Predict 30-Day Rehospitalization. Journal of Cardiovascular Nursing 2022;37(6):E181 View
  10. Zhou Y, Gao J. Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway?. Frontiers in Psychiatry 2022;13 View
  11. Sotoodeh M, Zhang W, Simpson R, Hertzberg V, Ho J. A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study. JMIR Medical Informatics 2023;11:e40672 View
  12. Kandi L, Rangel I, Movtchan N, Van Spronsen N, Kruger E. Comprehensive Management of Pressure Injury. Physical Medicine and Rehabilitation Clinics of North America 2022;33(4):773 View
  13. Toffaha K, Simsekler M, Omar M. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artificial Intelligence in Medicine 2023;141:102560 View
  14. Luo Z, Liu S, Yang L, Zhong S, Bai L. Ambulance referral of more than 2 hours could result in a high prevalence of medical-device-related pressure injuries (MDRPIs) with characteristics different from some inpatient settings: a descriptive observational study. BMC Emergency Medicine 2023;23(1) View
  15. Rêgo A, Furtado G, Bernardes R, Santos-Costa P, Dias R, Alves F, Ainla A, Arruda L, Moreira I, Bessa J, Fangueiro R, Gomes F, Henriques M, Sousa-Silva M, Pinto A, Bouçanova M, Sousa V, Tavares C, Barboza R, Carvalho M, Filipe L, Sousa L, Apóstolo J, Parreira P, Salgueiro-Oliveira A. Development of Smart Clothing to Prevent Pressure Injuries in Bedridden Persons and/or with Severely Impaired Mobility: 4NoPressure Research Protocol. Healthcare 2023;11(10):1361 View
  16. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  17. Barghouthi E, Owda A, Asia M, Owda M. Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms. Diagnostics 2023;13(17):2739 View
  18. Tehrany P, Zabihi M, Ghorbani Vajargah P, Tamimi P, Ghaderi A, Norouzkhani N, Zaboli Mahdiabadi M, Karkhah S, Akhoondian M, Farzan R. Risk predictions of hospital‐acquired pressure injury in the intensive care unit based on a machine learning algorithm. International Wound Journal 2023;20(9):3768 View
  19. Jafari M, Marquez G, Dechiraju H, Gomez M, Rolandi M. Merging machine learning and bioelectronics for closed-loop control of biological systems and homeostasis. Cell Reports Physical Science 2023;4(8):101535 View
  20. Pouzols S, Despraz J, Mabire C, Raisaro J. Development of a Predictive Model for Hospital-Acquired Pressure Injuries. CIN: Computers, Informatics, Nursing 2023;41(11):884 View
  21. Wang I, Walker R, Gillespie B, Scott I, Sugathapala R, Chaboyer W. Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews. International Journal of Nursing Studies 2024;150:104642 View
  22. Ho J, Sotoodeh M, Zhang W, Simpson R, Hertzberg V. An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations. Computers in Biology and Medicine 2024;168:107754 View
  23. dos Santos D, Queiroz J, Garcia I, Vieira J, Fernandes J, Sotgiu E, Minas G, Bouçanova M, Arruda L, Fangueiro R, Salgueiro-Oliveira A, Ainla A, Serra Alves F, Alves Dias R. Flexible Pressure and Temperature Microsensors for Textile-Integrated Wearables. Actuators 2024;13(1):42 View
  24. Toledo L, Bhering L, Ercole F. Artificial intelligence to predict bed bath time in Intensive Care Units. Revista Brasileira de Enfermagem 2024;77(1) View
  25. Toledo L, Bhering L, Ercole F. Inteligência artificial para predição do tempo de banho no leito em Unidades de Terapia Intensiva. Revista Brasileira de Enfermagem 2024;77(1) View
  26. Reese T, Domenico H, Hernandez A, Byrne D, Moore R, Williams J, Douthit B, Russo E, McCoy A, Ivory C, Steitz B, Wright A. Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation. JMIR Medical Informatics 2024;12:e51842 View
  27. Mansouri M, Krishnan G, McDonagh D, Zallek C, Hsiao-Wecksler E. Review of assistive devices for the prevention of pressure ulcers: an engineering perspective. Disability and Rehabilitation: Assistive Technology 2024;19(4):1511 View
  28. Ikuta K, Fukuoka K, Kimura Y, Nakagaki M, Ohga M, Suyama Y, Morita M, Umeda R, Konishi M, Nishikawa H, Yagi S. An ingenious deep learning approach for pressure injury depth evaluation with limited data. Journal of Tissue Viability 2024;33(3):387 View

Books/Policy Documents

  1. Fernandez K, Young A, Bhattarcharya A, Kusari A, Wei M. Teledermatology. View