Computational Drug Repositioning Using Continuous Self-controlled Case Series
This work addresses the challenge of efficiently finding new uses for existing drugs using patient data, which is incremental as it builds on prior self-controlled case series methods by incorporating continuous data.
The paper tackled the problem of computational drug repositioning by proposing a Continuous Self-controlled Case Series model that leverages temporal data from Electronic Health Records to discover new indications for existing drugs, specifically identifying drugs that control Fasting Blood Glucose levels, with results including rediscovery of known drugs and identification of drugs supported by recent literature.
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.