An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics

Archive ouverte

Bouzillé, Guillaume | Morival, Camille | Westerlynck, Richard | Lemordant, Pierre | Chazard, Emmanuel | Lecorre, Pascal | Busnel, Yann | Cuggia, Marc

Edité par CCSD ; IOS Press -

International audience. The aim of the study was to build a proof-of-concept demonstratrating that big data technology could improve drug safety monitoring in a hospital and could help pharmacovigilance professionals to make data-driven targeted hypotheses on adverse drug events (ADEs) due to drug-drug interactions (DDI). We developed a DDI automatic detection system based on treatment data and laboratory tests from the electronic health records stored in the clinical data warehouse of Rennes academic hospital. We also used OrientDb, a graph database to store informations from five drug knowledge databases and Spark to perform analysis of potential interactions betweens drugs taken by hospitalized patients. Then, we developed a machine learning model to identify the patients in whom an ADE might have occurred because of a DDI. The DDI detection system worked efficiently and computation time was manageable. The system could be routinely employed for monitoring.

• Description
• Sujet/Public
• Outil
• Outil d'anticipation
• Mémoire et thèse
• Gestion