ReviewJMIR medical informatics2021
Using Machine Learning Technologies in Pressure Injury Management: Systematic Review.
Review in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses 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
34 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Comparative assessment of nurses' knowledge and practice on medical device-related pressure injury management in India and Europe: a cross-sectional study.BMC nursing · 2026Article
- Pressure Injury Risk Assessment in Nursing Practice: A Head-to-Head Comparison of the Braden Scale and Machine Learning Models.Journal of clinical medicine · 2026Article
- Effects of a transformer-based AI-based application to support incontinence-associated dermatitis and pressure injury assessment, nursing care and documentation: Controlled pilot intervention study.International journal of nursing studies advances · 2026Article
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study.Journal of advanced nursing · 2026Article
- Prediction of intraoperative acquired pressure injury in elderly cardiac surgery patients via machine learning: model development with the MIMIC-IV and external validation.BMC medical informatics and decision making · 2025Article
- Low-value clinical practices in pressure injury management: a scoping review.BMC health services research · 2025Article
- Nursing-centered development of an AI-based decision support system in pressure ulcer and incontinence-associated dermatitis management - a mixed methods study.BMC nursing · 2025Article
- A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.Journal of advanced nursing · 2025Article
- Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 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
- Strategies adopted by the nursing team for the prevention of pressure ulcers in hospitalized older people: a scoping review.Revista da Escola de Enfermagem da U S P · 2025Article
- Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis.Journal of inflammation research · 2025Article
- Development and comparative evaluation of machine learning models for predicting lower extremity deep vein thrombosis in gastrointestinal cancer patients using multicenter longitudinal clinical data.Frontiers in surgery · 2025Article
- Design and development of an mHealth application for pressure ulcer care and caregiver support.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management.
objectiveThe objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice.
methodsWe conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
resultsA total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high.
conclusionsThere is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.
Indexed as
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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.