ArticleDiagnostics (Basel, Switzerland)2022
Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
17 citing papers in PubMed, 3 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
- 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
- Construction and validation of a multi-function artificial intelligence-assisted system for pressure injury recognition.Frontiers in physiology · 2026Article
- Prediction and Stage Classification of Pressure Ulcers in Intensive Care Patients by Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Pressure Injury Prediction in Intensive Care Units Using Artificial Intelligence: A Scoping Review.Nursing reports (Pavia, Italy) · 2025Review
- Observational
- Electronic-Medical-Record-Driven Machine Learning Predictive Model for Hospital-Acquired Pressure Injuries: Development and External Validation.Journal of clinical medicine · 2025Article
- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables.Journal of clinical medicine · 2023Article
- Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2023Review
- 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 · 2023Article
- Electrical Stimulation for Preventing Skin Injuries in Denervated Gluteal Muscles-Promising Perspectives from a Case Series and Narrative Review.Diagnostics (Basel, Switzerland) · 2023Article
- A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications.Journal of multidisciplinary healthcare · 2023Article
- 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 · 2023Article
- A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
- Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.
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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.