ArticleBMC medical informatics and decision making2021
Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled 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
- Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Early Detection of Deep Tissue Pressure Injury in Intensive Care Using Hemodynamics-Based Machine Learning: A Retrospective Cohort Study.International wound journal · 2026Observational
- Comment on: Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.Journal of clinical nursing · 2026Article
- Clinical Validation of Object Detection Models for AI-Based Pressure Injury Stage Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Early prediction of pressure injury risk in hospitalized patients using supervised machine learning models based on nursing records.Scientific reports · 2026Article
- Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study.Journal of advanced nursing · 2026Article
- Digital and Smart Technologies for Early Pressure Injury Detection and Prevention in Stroke and Mobility-Impaired Patients: A Scoping Review.Nursing research and practice · 2026Review
- Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings.BMC medical informatics and decision making · 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
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- Design and development of an mHealth application for pressure ulcer care and caregiver support.Frontiers in digital health · 2025Article
- Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals.Scientific reports · 2024Article
- Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.International wound journal · 2024Article
- Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.JMIR medical informatics · 2024Article
- 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
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 2 institutions in 1 country.
Funding
Abstract
backgroundHospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).
methodsWe used advanced data science techniques to first preprocess the data and then train machine learning classifiers to predict the probability of developing PIs. The AI training was based on large, incongruent, incomplete, heterogeneous, and time-varying data of hospitalized patients. Both model-based statistical methods and model-free AI strategies were used to forecast PI outcomes and determine the salient features that are highly predictive of the outcomes.
resultsOur findings reveal that PI prediction by model-free techniques outperform model-based forecasts. The performance of all AI methods is improved by rebalancing the training data and by including the Braden in the model learning phase. Compared to neural networks and linear modeling, with and without rebalancing or using Braden scores, Random forest consistently generated the optimal PI forecasts.
conclusionsAI techniques show promise to automatically identify patients at risk for hospital acquired PIs in different surgical services. Our PI prediction model provide a first generation of AI guidance to prescreen patients at risk for developing PIs. CLINICAL IMPACT: This study provides a foundation for designing, implementing, and assessing novel interventions addressing specific healthcare needs. Specifically, this approach allows examining the impact of various dynamic, personalized, and clinical-environment effects on PI prevention for hospital patients receiving care from various surgical services.
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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.