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

Guillaume Bouzillé (1) , Camille Morival (2) , Richard Westerlynck (3) , Pierre Lemordant (1) , Emmanuel Chazard (4) , Pascal Lecorre (2) , Yann Busnel (5, 6) , Marc Cuggia (1)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 Irset - Institut de recherche en santé, environnement et travail
3 IMT Atlantique - IMT Atlantique
4 METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694
5 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
6 IMT Atlantique - SRCD - Département Systèmes Réseaux, Cybersécurité et Droit du numérique

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.

Keywords: Drug Interaction; Machine Learning; Computing Methodologies

Domains: Life Sciences [q-bio]; Bioengineering

Guillaume Bouzillé, Camille Morival, Richard Westerlynck, Pierre Lemordant, Emmanuel Chazard, et al.. An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics. Studies in Health Technology and Informatics, 2019, 264, pp.45-49. ⟨10.3233/SHTI190180⟩. ⟨hal-02280252⟩