Computational Drug Repositioning Using Continuous Self-controlled Case Series

TL;DR

This paper introduces the CSCCS model, a novel method leveraging patient-level temporal data from EHRs to identify potential drug repurposing opportunities, demonstrated by rediscovering known and potential blood glucose control drugs.

Contribution

The paper proposes the Continuous Self-controlled Case Series (CSCCS) model for computational drug repositioning using EHR data, incorporating temporal ordering of measurements and prescriptions.

Findings

Successfully rediscovered known blood glucose control drugs.

Identified new candidate drugs with literature support.

Validated model on Marshfield Clinic EHR data.

Abstract

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.