יובל שחר

In this study, we implemented a hybrid approach, incorporating temporal data mining, machine learning, and process mining for modeling and predicting the course of treatment of Intensive Care Unit (ICU) patients. We used process mining algorithms to construct models of management of ICU patients. Then, we extracted the decision points from the mined models and used temporal data mining of the periods preceding the decision points to create temporal-pattern features. We trained classifiers to predict the next actions expected for each point. The methodology was evaluated on medical ICU data from the hypokalemia and hypoglycemia domains. The study's contributions include the representation of medical treatment trajectories of ICU patients using process models, and the integration of Temporal Data Mining and Machine Learning with Process Mining, to predict the next therapeutic actions in the ICU.