An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics. Bouzillé, G., Morival, C., Westerlynck, R., Lemordant, P., Chazard, E., Lecorre, P., Busnel, Y., & Cuggia, M. Studies in Health Technology and Informatics, 264:45–49, August, 2019. doi abstract bibtex 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.
@article{bouzille_automated_2019,
title = {An {Automated} {Detection} {System} of {Drug}-{Drug} {Interactions} from {Electronic} {Patient} {Records} {Using} {Big} {Data} {Analytics}},
volume = {264},
issn = {1879-8365},
doi = {10.3233/SHTI190180},
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.},
language = {eng},
journal = {Studies in Health Technology and Informatics},
author = {Bouzillé, Guillaume and Morival, Camille and Westerlynck, Richard and Lemordant, Pierre and Chazard, Emmanuel and Lecorre, Pascal and Busnel, Yann and Cuggia, Marc},
month = aug,
year = {2019},
pmid = {31437882},
keywords = {Computing Methodologies, Drug Interaction, Machine Learning},
pages = {45--49},
}
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