Intensive Care Unit Patient Outcome Prediction Using ν-Support Vector Classification and Stochastic Signal...

Intensive care units (ICUs) treat patients with life-threatening illnesses. Worldwide, intensive care demand is massive. Predicting patient outcomes in ICUs holds significant importance for health care operation management. Nevertheless, it remains a challenging problem that researchers and health care practitioners have yet to overcome. While the newly emerging health digital trace data offer new possibilities, such data contain complex time series and patterns. Although researchers have devised severity score systems, traditional machine learning models with feature engineering, and deep learning models that use raw clinical data to predict ICU outcomes, existing methods have limitations.

This study aimed to develop a novel feature extraction and machine learning framework to repurpose and extract features with strong predictive power from patients' health digital traces for ICU outcome prediction.

Guided by signal processing techniques and medical domain knowledge, the proposed framework introduces a novel, signal processing-based feature engineering method to extract highly predictive features from ICU digital trace data. We rigorously evaluated this method on a real-world ICU dataset, demonstrating significant improvements over both traditional and deep learning baseline methods. The method was then evaluated using a real-world database to assess prediction accuracy and feature representativeness.

The prediction results obtained by the proposed framework significantly outperformed state-of-the-art benchmarks. This demonstrated the framework's effectiveness in capturing key patterns from complex health digital traces for improving ICU outcome prediction.

Our study contributes to health care operation management by leveraging digital traces from health care information systems to address challenges with significant implications for health care.