ReviewDiagnostics (Basel, Switzerland)2023
Systematic Review for Risks of Pressure Injury and Prediction Models Using Machine Learning Algorithms.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Diagnostic Accuracy of Pressure Injury Risk Assessment Tools for Critically Ill Patients: A Systematic Review and Network Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Artificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.Frontiers in medicine · 2024Pooled it
- Multifactorial Risk Assessment of Hospital-Acquired Pressure Injuries Among High-Risk Patients: A Case-Control Study.Healthcare (Basel, Switzerland) · 2026Article
- Association of serum albumin level, feeding route, and mobility status with pressure ulcer presence in bedridden adults receiving long-term care.BMC primary care · 2026Article
- Development and Internal Validation of a Gradient Boosting Model for Pressure Injury Risk in the ICU.International wound journal · 2026Article
- Article
- 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
- Risk prediction tools for pressure injury occurrence: an umbrella review of systematic reviews reporting model development and validation methods.Diagnostic and prognostic research · 2025Review
- Design and development of an mHealth application for pressure ulcer care and caregiver support.Frontiers in digital health · 2025Article
- Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.International wound journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.
Indexed as
Identifiers
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.