Intensive Care Unit Patient Outcome Prediction Using ν-Support Vector Classification and Stochastic Signal Processing-Based Feature Extraction Techniques: Algorithm Development and Validation Study.

ABSTRACT:

Background

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.

Objective

This study aimed to develop a novel feature extraction and machi

SUBMITTER: Wang S 

PROVIDER: S-EPMC12421204 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature