Discovering predictive trend-event patterns in temporal clinical data

Overwhelming amounts of clinical data are retrieved daily, and healthcare stakeholders may want to derive new knowledge from them. One of the methodological tools proposed to analyze clinical data is pattern mining, even with its temporal extensions. In particular, research efforts have been devoted to either mining complex temporal features (e.g., trends of a specific vital sign) or discovering predictive patterns capable of describing the class of interest compactly. In this paper, we propose a methodology for deriving a new kind of predictive temporal patterns, called predictive trend-event patterns (PTE-Ps), that consists of predictive patterns composed by event occurrences and trends of vital signs, they could influence. PTE-Ps are extracted using a classification model that considers and combines various predictive pattern candidates and selects only those that are relevant to improve the performance of the prediction of a specific class (e.g., only those patterns important to predict sepsis). We provide an original algorithm to mine PTE-Ps and describe the tool we implemented for retrieving them. Finally, we discuss some first results we obtained by pre-processing and mining ICU data from the MIMIC III database, focusing on trend-event patterns predictive of sepsis.