Clinicians in intensive care units (ICUs) rely on standardized scores as risk prediction models to predict a patient's vulnerability to life-threatening events. 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 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 focus on adult patients. To address this need, we propose a new 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 a potential to predict risks based on all available conditions without being restricted by a fixed observation window. We also develop a new metric that rigidly assesses the reliability of all generated association rules. In addition, icuARM-II features an interactive user interface. Using icuARM-II, our results showed a use case of short-term mortality prediction using lab testing results, which demonstrated a potential for reliable ICU risk prediction using personalized clinical data in a previously neglected population.
In REF it was presented the new prediction system ICU ARM-II (Intensive Care Unit Association Rule Mining), based in a set of association rules that forms a flexible model for the prediction of personalized risk.
icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units
BCB '14
Computer Science Medicine