ArticleJournal of the American Medical Informatics Association : JAMIA2021
Predicting pressure injury using nursing assessment phenotypes and machine learning methods.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.Frontiers in medicine · 2024Pooled it
- Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.International wound journal · 2023Pooled it
- A systematic review of predictive models for hospital-acquired pressure injury using machine learning.Nursing open · 2023Pooled it
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- A predictive nomogram for in-ICU deterioration of stage 1 pressure injuries: a retrospective study.Frontiers in medicine · 2026Article
- Prediction and Stage Classification of Pressure Ulcers in Intensive Care Patients by Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation.Journal of clinical medicine · 2025Article
- Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.Diagnostic and prognostic research · 2025Review
- Mapping Artificial Intelligence Research Trends in Critical Care Nursing: A Bibliometric Analysis.Journal of multidisciplinary healthcare · 2025Review
- Decoding machine learning in nursing research: A scoping review of effective algorithms.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Development and validation of a nomogram for pressure injury risk prediction in stroke patients: a retrospective cohort study.Frontiers in neurology · 2025Article
- Predictive value of Braden scale combined with hemoglobin and hematocrit for oral mucosal pressure injury risk in ICU patients.American journal of translational research · 2025Article
- A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.JMIR medical informatics · 2024Article
- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.Journal of the American Geriatrics Society · 2024Article
- A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury.Frontiers in physiology · 2024Article
- Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Review
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivePressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data.
methodsWe utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance.
resultsTwo pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups.
conclusionsThis model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.
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