Search

Hospital-acquired pressure injuries (HAPIs) are a common and largely preventable complication in ICU patients, affecting 5–15% of cases and contributing to increased morbidity and healthcare costs. Despite standardized nursing protocols, incidence remains high, highlighting the need for more effective predictive and preventive approaches. While traditional tools like the Braden Scale offer interpretability, they lack sufficient predictive accuracy in critically ill populations. In contrast, machine learning models such as XGBoost and random forests improve prediction but function as black boxes, limiting clinical trust and actionable insight. To address this gap, this work proposes an Explainable Boosting Machine (EBM) framework trained on electronic health record (EHR) data from over 50,000 ICU admissions (2017–2023). EBMs combine strong predictive performance with interpretability by modeling feature effects through shape functions and capturing pairwise interactions. This allows identification of both global and patient-specific risk factors while maintaining transparency. The framework emphasizes modifiable factors such as repositioning frequency, nutrition, and medical device management, revealing nonlinear thresholds and interaction effects often missed by conventional methods. Overall, the proposed approach integrates accurate prediction with clear, clinically interpretable insights, enabling real-time identification of actionable risk factors for HAPI prevention. By bridging predictive modeling and nursing decision-making, it supports more targeted interventions and improved patient outcomes in critical care settings.

Skilled nursing facilities (SNFs) in the U.S. serve over 1.5 million residents and experience continuous census volatility driven by admissions, discharges, and mortality, impacting staffing, bed availability, and care quality. Existing forecasting methods rarely capture these dynamics together, leading to reactive and inefficient operational decisions. A need exists for accurate, multi-horizon, and data-integrated forecasting systems. Traditional models like ARIMA and LSTM are limited in SNF census forecasting because they produce single-point estimates, fail to model uncertainty, and cannot effectively integrate heterogeneous data such as facility characteristics, temporal utilization patterns, and external factors like COVID-19 prevalence. They also lack interpretability, reducing their usefulness for decision-making. This study introduces an attention-based Temporal Fusion Transformer (TFT) for multi-horizon SNF census forecasting (1, 7, 14, and 30 days). It integrates admissions, discharges, and COVID-19 prevalence through dedicated encoders and applies variable selection networks, LSTM layers, and multi-head attention to capture temporal dependencies and feature importance. The model outputs quantile forecasts (10th, 50th, 90th percentiles) to quantify uncertainty. The TFT enhances interpretability by identifying which past events and features most influence predictions at each horizon, enabling administrators to understand how admissions trends, discharge patterns, and COVID-19 surges affect census dynamics. The proposed framework enables proactive SNF capacity planning by combining multi-source data with interpretable, uncertainty-aware forecasting, supporting a shift from reactive staffing to anticipatory resource allocation and improved operational efficiency.