An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics - Université de Rennes Access content directly
Journal Articles Studies in Health Technology and Informatics Year : 2019

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

Abstract

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.

Domains

Bioengineering
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Dates and versions

hal-02280252 , version 1 (03-06-2022)

Identifiers

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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⟩
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