Jenny Alderden is an associate professor at Boise State University in Boise, Idaho.
Jace Johnny is a nurse practitioner at the University of Utah Medical Center and a PhD candidate at the University of Utah in Salt Lake City.
Am J Crit Care. 2024 Sep 1;33(5):373-381. doi: 10.4037/ajcc2024856.
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.
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.
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.
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.
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.
医院获得性压疮（HAPI）对重症监护病房（ICU）患者的预后有重大影响。有效的预防依赖于早期和准确的风险评估。传统的风险评估工具，如布雷登量表，往往无法捕捉到 ICU 特有的因素，限制了其预测准确性。虽然人工智能模型提供了更高的准确性，但它们的“黑箱”性质对临床应用构成了障碍。
开发一种基于人工智能的 HAPI 风险评估模型，并结合可解释人工智能仪表板，以提高整体和个体患者水平的可解释性。
采用可解释人工智能方法分析来自重症监护医疗信息集市的 ICU 患者数据。预测变量仅限于 ICU 入院后 48 小时内。评估了各种机器学习算法，最终采用集成“超级学习者”模型。通过 5 折交叉验证，使用接收者操作特征曲线下的面积来量化模型的性能。开发了一个解释器仪表板（使用合成数据保护患者隐私），具有全局和局部水平的深入模型解释的交互式可视化功能。
最终样本包括 28395 例患者，HAPI 的发生率为 4.9%。集成超级学习者模型表现良好（曲线下面积=0.80）。解释器仪表板提供了模型预测的全局和患者水平的交互式可视化，显示了每个变量对风险评估结果的影响。
该模型及其仪表板为临床医生提供了一种透明、可解释的人工智能 HAPI 风险评估系统，可能使更有效的预防干预措施成为可能。
