ArticleRisk management and healthcare policy2021
The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms.
Article in Risk management and healthcare policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled 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
- Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer.World journal of gastroenterology · 2024Trial
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- Early Detection of Deep Tissue Pressure Injury in Intensive Care Using Hemodynamics-Based Machine Learning: A Retrospective Cohort Study.International wound journal · 2026Observational
- Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China.BMC public health · 2026Article
- Multimodal deep learning integration for predicting renal function outcomes in living donor kidney transplantation: a retrospective cohort study.International journal of surgery (London, England) · 2026Article
- A prospective cohort study on the incidence and influencing factors of subsyndromal delirium in ICU patients.Frontiers in psychiatry · 2026Article
- Predictors of Unfavorable Outcomes in Diabetic Foot Ulcers.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a decision tree model for prediction of insomnia risk among ischemic stroke convalescence patients.BMC public health · 2025Article
- Developmental validation of an mRNA-cSNP profiling panel for body fluids identification and individualization.International journal of legal medicine · 2025Article
- Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.International journal of molecular sciences · 2025Article
- Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.Neurologia medico-chirurgica · 2025Article
- Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation.Journal of clinical medicine · 2025Article
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- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- Explore variation of salivary bacteria across time and geolocations.International journal of legal medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeBuild machine learning models for predicting pressure ulcer nursing adverse event, and find an optimal model that predicts the occurrence of pressure ulcer accurately. PATIENTS AND
methodsRetrospectively enrolled 5814 patients, of which 1673 suffer from pressure ulcer events. Support vector machine (SVM), decision tree (DT), random forest (RF) and artificial neural network (ANN) models were used to construct the pressure ulcer prediction models, respectively. A total of 19 variables are included, and the importance of screening variables is evaluated. Meanwhile, the performance of the prediction models is evaluated and compared.
resultsThe experimental results show that the four pressure ulcer prediction models all achieve good performance. Also, the AUC values of the four models are all greater than 0.95. Besides, the comparison of the four models indicates that RF model achieves a higher accuracy for the prediction of pressure ulcer.
conclusionThis research verifies the feasibility of developing a management system for predicting nursing adverse event based on big data technology and machine learning technology. The random forest and decision tree model are more suitable for constructing a pressure ulcer prediction model. This study provides a reference for future pressure ulcer risk warning based on big data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.