ALL CITATIONS

• Development and validation of CAVE score in predicting presence of pressure ulcer in intensive care patients, Heliyon 6(8),
  DOI: http://doi.org/10.1016/j.heliyon.2020.e04612
  
  
• Predicted Influences of Artificial Intelligence on the Domains of Nursing: A Scoping Review (Preprint), JMIR Nursing
  DOI: http://doi.org/10.2196/23939
  
  
• Selected Biochemical Blood Parameters and a Risk of Pressure Ulcers in Patients Receiving Treatment in Intensive Care Units, Medicina 57(2),
  DOI: http://doi.org/10.3390/medicina57020177
  
  
• Putting Health 4.0 at the service of Society 5.0: Exploratory insights from a pilot study, Socio-Economic Planning Sciences
  DOI: http://doi.org/10.1016/j.seps.2021.101163
  
  
• A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study, International Wound Journal
  DOI: http://doi.org/10.1111/iwj.13680
  
  
• Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review, Journal of Medical Internet Research 23(11),
  DOI: http://doi.org/10.2196/26522
  
  
• Using past performance to improve future clinical outcomes in pressure ulcer prevention, Journal of Wound Care 30(6),
  DOI: http://doi.org/10.12968/jowc.2021.30.6.440
  
  
• Predicting pressure injury using nursing assessment phenotypes and machine learning methods, Journal of the American Medical Informatics Association
  DOI: http://doi.org/10.1093/jamia/ocaa336
  
  
• Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review, Informatics 8(1),
  DOI: http://doi.org/10.3390/informatics8010016
  
  
• Machine Learning Approaches for Hospital Acquired Pressure Injuries: A Retrospective Study of Electronic Medical Records, Frontiers in Medical Technology
  DOI: http://doi.org/10.3389/fmedt.2022.926667
  
  
• EHAPI: A comprehensive and improved definition for hospital-acquired pressure injury classification based on electronic health records (Preprint), JMIR Medical Informatics
  DOI: http://doi.org/10.2196/40672
  
  
• A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study, Diagnostics 13(1),
  DOI: http://doi.org/10.3390/diagnostics13010031
  
  
• A systematic review of predictive models for hospital‐acquired pressure injury using machine learning, Nursing Open
  DOI: http://doi.org/10.1002/nop2.1429
  
  
• Individualized Risk Prediction for Improved Chronic Wound Management, Advances in Wound Care
  DOI: http://doi.org/10.1089/wound.2022.0017
  
  
• Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records, Journal of Basic and Clinical Health Sciences
  DOI: http://doi.org/10.30621/jbachs.993798
  
  
• Development and validation of a machine learning algorithm–based risk prediction model of pressure injury in the intensive care unit , International Wound Journal
  DOI: http://doi.org/10.1111/iwj.13764
  
  
• 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 20(6),
  DOI: http://doi.org/10.3390/ijerph20064911
  
  
• An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur, International Journal of Environmental Research and Public Health 20(1),
  DOI: http://doi.org/10.3390/ijerph20010828
  
  
• Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review, International Journal of Environmental Research and Public Health 20(1),
  DOI: http://doi.org/10.3390/ijerph20010796
  
  
• A systematic review of nurses' knowledge and related factors towards the prevention of medical device‐related pressure ulcers, International Wound Journal
  DOI: http://doi.org/10.1111/iwj.14122
  
  
• Prediction of Prednisolone Dose Correction Using Machine Learning, Journal of Healthcare Informatics Research
  DOI: http://doi.org/10.1007/s41666-023-00128-3