Complex data-driven predictive modeling in personalized clinical decision support for Acute Coronary Syndrome episodes

The objective of this paper is to demonstrate the development of complex model of clinical episode, based on data-driven approach, for decision support in treatment of ACS (Acute Coronary Syndrome). The idea is aimed at improving predictive capability of a data-driven model by combining different models within a composite data-driven model. It can be implemented either hierarchical or alternative combination of models. Three examples of data-driven models are described: simple classifier, outcome prediction based on reanimation time and states based prediction model, to be used as part of complex model of episodes. To implement the proposed approach, a generalized architecture of data-driven clinical decision support systems was developed. The solution is developed as a part of complex clinical decision support system for cardiac diseases for Federal Almazov North-West Medical Research Centre in Saint Petersburg, Russia.