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

Decision Support for Health Professionals (1210) Pressure Ulcers and Decubitus Wounds (25)

Published on in Vol 7, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13785, first published .
Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

Authors of this article:

Sookyung Hyun1 Author Orcid Image ;   Susan Moffatt-Bruce2, 3 Author Orcid Image ;   Cheryl Cooper4 Author Orcid Image ;   Brenda Hixon5 Author Orcid Image ;   Pacharmon Kaewprag6 Author Orcid Image
Article Authors Cited by (32) Tweetations (2) Metrics

Journals

  1. Choi B, Kim M, Kim S. Risk prediction models for the development of oral-mucosal pressure injuries in intubated patients in intensive care units: A prospective observational study. Journal of Tissue Viability 2020;29(4):252 View
  2. Hyun S, Kaewprag P, Cooper C, Hixon B, Moffatt-Bruce S. Exploration of critical care data by using unsupervised machine learning. Computer Methods and Programs in Biomedicine 2020;194:105507 View
  3. Tarekegn A, Ricceri F, Costa G, Ferracin E, Giacobini M. Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. JMIR Medical Informatics 2020;8(6):e16678 View
  4. Orciuoli F, Orciuoli F, Peduto A. A Mobile Clinical DSS based on Augmented Reality and Deep Learning for the home cares of patients afflicted by bedsores. Procedia Computer Science 2020;175:181 View
  5. Cox J. Risk Factors for Pressure Injury Development Among Critical Care Patients. Critical Care Nursing Clinics of North America 2020;32(4):473 View
  6. Cox J, Schallom M. Pressure Injuries in Critical Care Patients: A Conceptual Schema. Advances in Skin & Wound Care 2021;34(3):124 View
  7. Monteiro D, Borges E, Spira J, Garcia T, Matos S. INCIDENCE OF SKIN INJURIES, RISK AND CLINICAL CHARACTERISTICS OF CRITICAL PATIENTS. Texto & Contexto - Enfermagem 2021;30 View
  8. Dweekat O, Lam S, McGrath L. 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 2023;20(1):828 View
  9. Shui A, Kim P, Aribindi V, Huang C, Kim M, Rangarajan S, Schorger K, Aldrich J, Lee H. Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients. Critical Care Explorations 2021;3(11):e0580 View
  10. Ciasullo M, Orciuoli F, Douglas A, Palumbo R. Putting Health 4.0 at the service of Society 5.0: Exploratory insights from a pilot study. Socio-Economic Planning Sciences 2022;80:101163 View
  11. Di Martino F, Orciuoli F. A computational framework to support the treatment of bedsores during COVID-19 diffusion. Journal of Ambient Intelligence and Humanized Computing 2024;15(1):219 View
  12. ÇAYIRTEPE Z, ŞENEL A. 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 2022;6(3):958 View
  13. Levy J, Lima J, Miller M, Freed G, O'Malley A, Emeny R. Machine Learning Approaches for Hospital Acquired Pressure Injuries: A Retrospective Study of Electronic Medical Records. Frontiers in Medical Technology 2022;4 View
  14. Andersson J, Imberg S, Rosengren K. Documentation of pressure ulcers in medical records at an internal medicine ward in university hospital in western Sweden. Nursing Open 2023;10(3):1794 View
  15. Dweekat O, Lam S, McGrath L. A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study. Diagnostics 2022;13(1):31 View
  16. Jiang X, Wang Y, Wang Y, Zhou M, Huang P, Yang Y, Peng F, Wang H, Li X, Zhang L, Cai F. Application of an infrared thermography-based model to detect pressure injuries: a prospective cohort study. British Journal of Dermatology 2022;187(4):571 View
  17. 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
  18. 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
  19. Lin B, Ma J, Fang Y, Lei P, Wang L, Qu L, Wu W, Jin L, Sun D. Advances in Zebrafish for Diabetes Mellitus with Wound Model. Bioengineering 2023;10(3):330 View
  20. 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
  21. Kim M, Ryu J, Choi B. Development and Effectiveness of a Clinical Decision Support System for Pressure Ulcer Prevention Care Using Machine Learning. CIN: Computers, Informatics, Nursing 2023;41(4):236 View
  22. Cho E, Kim S, Heo S, Shin J, Hwang S, Kwon E, Lee S, Kim S, Kang B. Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation. Scientific Reports 2023;13(1) View
  23. Gou L, Zhang Z, A. Y, Liu B. Risk factors for medical device-related pressure injury in ICU patients: A systematic review and meta-analysis. PLOS ONE 2023;18(6):e0287326 View
  24. Picoito R, Lapuente S, Ramos A, Rabiais I, Deodato S, Nunes E. Risk assessment instruments for pressure ulcer in adults in critical situation: a scoping review. Revista Latino-Americana de Enfermagem 2023;31 View
  25. Picoito R, Lapuente S, Ramos A, Rabiais I, Deodato S, Nunes E. Instrumentos para la evaluación del riesgo de lesiones por presión en adultos en estado crítico: scoping review. Revista Latino-Americana de Enfermagem 2023;31 View
  26. Pinhasov T, Isaacs S, Donis-Garcia M, Oropallo A, Brennan M, Rao A, Landis G, Agrell-Kann M, Li T. Reducing lower extremity hospital-acquired pressure injuries: a multidisciplinary clinical team approach. Journal of Wound Care 2023;32(Sup7):S31 View
  27. Picoito R, Lapuente S, Ramos A, Rabiais I, Deodato S, Nunes E. Instrumentos para a avaliação do risco de lesões por pressão para adultos em situação crítica: scoping review*. Revista Latino-Americana de Enfermagem 2023;31 View
  28. Yusharyahya S, Legiawati L, Astriningrum R, Jonlean R, Andhira V. Characteristics of pressure injuries among geriatric patients at an Indonesian tertiary hospital: a cross-sectional study. Medical Journal of Indonesia 2023;32(3):183 View
  29. Ma Y, He X, Yang T, Yang Y, Yang Z, Gao T, Yan F, Yan B, Wang J, Han L. Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta‐analysis. Journal of Clinical Nursing 2024 View
  30. Zhang N, Li Y, Li X, Li F, Jin Z, Li T, Ma J. Incidence of medical device-related pressure injuries: a meta-analysis. European Journal of Medical Research 2024;29(1) View
  31. Al-Mamari F, Al-Rawajfah O, Al Sabei S, Al-Wahaibi K. Hospital-acquired pressure ulcers among adult ICU patients in tertiary hospitals in Oman: a one-year prevalence study. Journal of Wound Care 2024;33(Sup10):S10 View