Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.
Background: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors,...
| Publicado en: | American Journal of Critical Care Vol. 33; no. 5; pp. 373 - 382 |
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| Autores principales: | , , , , , , , |
| Formato: | CEU research Journal Article |
| Publicado: |
American Association of Critical-Care Nurses
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179362545&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179362545 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10623264 44L jtl: American Journal of Critical Care issn: 10623264 maglogo: N pubinfo: dt: Sep2024 vid: 33 iid: 5 pid: 2559 pub: American Association of Critical-Care Nurses place: Alisa Veijo, California artinfo: ui: 179362545 179362545 179362545 10.4037/ajcc2024856 179362545 ppf: 373 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk. aug: au: Alderden, Jenny Johnny, Jace Brooks, Katie R. Wilson, Andrew Yap, Tracey L. Zhao, Yunchuan van der Laan, Mark Kennerly, Susan affil: an associate professor at Boise State University in Boise, Idaho sug: subj: Pressure Ulcer Epidemiology Pressure Ulcer Psychosocial Factors Pressure Ulcer Risk Factors Risk Assessment Methods Early Diagnosis Methods Hospitals Prediction Models Intensive Care Units Dashboard Systems Artificial Intelligence, Generative Education, Continuing (Credit) Human Algorithms Braden Scale for Predicting Pressure Sore Risk Scales Health Services Accessibility Hospitalization Privacy and Confidentiality Machine Learning Methods Critical Care Sensitivity and Specificity Outcomes (Health Care) Male Female Middle Age Aged Random Forest Descriptive Statistics ROC Curve Data Analysis Software Logistic Regression Oxygenation Perfusion Decision Making Retrospective Design Massachusetts Record Review Quality of Health Care Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption. Objective: To develop an artificial intelligence–based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels. Methods: An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels. Results: The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome. Conclusion: The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence–based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions. pubtype: Academic Journal doctype: CEU research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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