icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units

Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. Conventional Current scales calculate scores from a fixed set of conditions collected within a specific time window. However, modern monitoring technologies generate complex, temporal, and multimodal patient data that conventional prediction models scales cannot fully utilize. Thus, a more sophisticated model is needed to tailor individual characteristics and incorporate multiple temporal modalities for a personalized risk prediction. Furthermore, most scales models focus on adult patients. To address this needdeficiency, we propose a newly designed ICU risk prediction system, called icuARM-II, using a large-scaled pediatric ICU database from Children's Healthcare of Atlanta. This novel database contains clinical data collected in 5,739 ICU visits from 4,975 patients. We propose a temporal association rule mining framework giving clinicians a potential to perform predict risks prediction based on all available patient conditions without being restricted by a fixed observation window. We also develop a new metric that can rigidly assesses the reliability of all all generated association rules. In addition, the icuARM-II features an interactive user interface. Using the icuARM-II, our results demonstrated showed a use case of short-term mortality prediction using lab testing results, which demonstrated a potential new solution for reliable ICU risk prediction using personalized clinical data in a previously neglected population.