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Mouse Model of Pressure Ulcers After Spinal Cord Injury

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Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

Jenny Alderden¹, Jace Johnny², Katie R Brooks³

¹Jenny Alderden is an associate professor at Boise State University in Boise, Idaho.

American Journal of Critical Care : an Official Publication, American Association of Critical-Care Nurses

|August 31, 2024

Summary

This study developed an explainable artificial intelligence model to predict hospital-acquired pressure injuries (HAPIs) in intensive care units (ICUs). The AI system offers transparent risk assessment for better prevention strategies.

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Area of Science:

Artificial Intelligence in Healthcare
Clinical Informatics
Patient Safety

Background:

Hospital-acquired pressure injuries (HAPIs) significantly impact patient outcomes in intensive care units (ICUs).
Current risk assessment tools lack accuracy due to failure in capturing ICU-specific factors.
The 'black box' nature of AI models hinders clinical adoption.

Purpose of the Study:

To develop an AI-based HAPI risk assessment model.
To enhance model interpretability using an explainable AI (XAI) dashboard.
To provide global and individual patient-level risk insights.

Main Methods:

Utilized an explainable AI approach on ICU patient data (MIMIC).
Focused on predictor variables within the first 48 hours of ICU admission.
Developed an ensemble 'super learner' model and an interactive explainer dashboard.

Main Results:

Analyzed 28,395 patients with a 4.9% HAPI incidence.
The ensemble model achieved an area under the curve of 0.80.
The dashboard provided interactive global and local visualizations of model predictions.

Conclusions:

The AI model and dashboard offer a transparent and interpretable HAPI risk assessment system.
This system can facilitate more effective and timely preventive interventions.
Improved clinical decision-making for HAPI prevention in ICUs.