Machine Learning Approaches for Patient State Prediction in Pediatric ICUs
Muhammad Aurangzeb Ahmad, Eduardo Antonio Trujillo Rivera, Pollack M.D. Murray, Eckert M.D. Carly, Patel M.D. Anita, Ankur M. Teredesai · 2021
We consider the problem of characterizing and predicting the condition of pediatric patients in intensive care units (ICUs). This population is often typified by rapid changes in patient conditions which necessitate predictions that can capture transition in patient states. While the assessment of patient’s condition is currently usually done using domain based scoring systems, we employ machine learning models for predicting the state of the pediatric patient. Additionally, we explore how model explainability could affect the usage of predictive models in a real world settings.