ArticleInternational journal of environmental research and public health2021
Identifying the Risk Factors Associated with Nursing Home Residents' Pressure Ulcers Using Machine Learning Methods.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- 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
- Sacral Microvascular Response in Older Adults Following Mechanical Loading Assessed by Photoplethysmography and Infrared Thermometry.International journal of environmental research and public health · 2025Observational
- How Neighborhood Social Deprivation Is Associated With Nursing Home Quality: A Structure, Process, Outcome Model.Innovation in aging · 2025Article
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- 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
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions.
objectiveThe purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs).
methodsWe applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60).
resultsThe random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674).
conclusionsThe random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents' characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.
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What OpenQuestion holds
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.