Computational Drug Repositioning Using Continuous Self-controlled Case Series
Zhaobin Kuang, UW-Madison; James Thomson, Morgridge Institute; Michael Caldwell, Marshfield Clinic; Peggy Peissig, ; Ron Stewart, Morgridge Institute; Page David*, University of Wisconsin
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in Electronic Health Records (EHRs), we propose a Continuous Self-controlled Case Series (CSCCS) model for CDR. As an initial evaluation, we look for drugs that can control Fasting Blood Glucose (FBG) level in our experiments. Applying CSCCS to the Marshﬁeld Clinic EHR, well-known drugs that are indicated for controlling blood glucose level are rediscovered. Furthermore, some drugs with recent literature support for the potential eﬀect of blood glucose level control are also identiﬁed.
Filed under: Time Series and Stream Mining