Purpose: The purpose of this study is to build a risk prediction model for pediatric Crohn's disease to predict outcomes of sustained remission/flare, surgery, and hospitalization using data from ICN Registry and PCORnet.
Specific Aims:
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Leveraging longitudinal data from ICN registry and PCORnet, and previously developed methods in assessing and stratifying disease activities, this study proposes to apply data analytics and visualization to facilitate efficient decision support for personalized care and risk prediction in pediatric Crohn’s disease. We aim to develop a machine-learning based clinical pathway learning algorithm for pediatric Crohn’s disease that predicts for 3 outcomes of interests: (1) sustained remission/flare, (2) surgery, and (3) hospitalization. Stratifying patients by their risk levels using PCDAI and sPCDAI, we will infer the common disease progression trajectories that are unique to each patient subpopulation from data, which we further use for prediction of future patient states.
Start Date: March 2017
Contact: Yiye Zhang