Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.
| Autor: | Alderden, Jenny, Johnny, Jace, Brooks, Katie R., Wilson, Andrew, Yap, Tracey L., Zhao, Yunchuan, van der Laan, Mark, Kennerly, Susan |
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| Předmět: |
RISK assessment
DASHBOARDS (Management information systems) GENERATIVE artificial intelligence CONTINUING education units HEALTH services accessibility RANDOM forest algorithms PREDICTION models BEDSORE risk factors RECEIVER operating characteristic curves MEDICAL quality control HOSPITAL care PRIVACY LOGISTIC regression analysis HOSPITALS EVALUATION of medical care DESCRIPTIVE statistics DECISION making RETROSPECTIVE studies REACTIVE oxygen species OXYGEN in the body INTENSIVE care units RESEARCH MEDICAL records ACQUISITION of data EARLY diagnosis MACHINE learning DATA analysis software PERFUSION PRESSURE ulcers ALGORITHMS MEDICAL ethics CRITICAL care medicine SENSITIVITY & specificity (Statistics) DISEASE risk factors |
| Zdroj: | American Journal of Critical Care; Sep2024, Vol. 33 Issue 5, p373-381, 9p |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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