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},
}
Downloads: 0
{"_id":"bbwoXPMGer6PXB8th","bibbaseid":"bouzill-morival-westerlynck-lemordant-chazard-lecorre-busnel-cuggia-anautomateddetectionsystemofdrugdruginteractionsfromelectronicpatientrecordsusingbigdataanalytics-2019","authorIDs":["56bbc1d374cc1b530f000455","5de7e5e0c8f9f6df0100015e","5de7f8ccc8f9f6df010002bb","5ded28f59d5885de01000036","5def9dac706001de010000cd","5dfdca63b6b936e401000059","5e343c4b0c807ede01000031","5e5145c6fe5af9df0100007a","TaHudmME8852eigtD","dbGudjxzAqNqqb9kP","kBW6Tt8nkHnKvzNQj","sNmKyQYr9ZbXTXPBe"],"author_short":["Bouzillé, G.","Morival, C.","Westerlynck, R.","Lemordant, P.","Chazard, E.","Lecorre, P.","Busnel, Y.","Cuggia, M."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Bouzillé"],"firstnames":["Guillaume"],"suffixes":[]},{"propositions":[],"lastnames":["Morival"],"firstnames":["Camille"],"suffixes":[]},{"propositions":[],"lastnames":["Westerlynck"],"firstnames":["Richard"],"suffixes":[]},{"propositions":[],"lastnames":["Lemordant"],"firstnames":["Pierre"],"suffixes":[]},{"propositions":[],"lastnames":["Chazard"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Lecorre"],"firstnames":["Pascal"],"suffixes":[]},{"propositions":[],"lastnames":["Busnel"],"firstnames":["Yann"],"suffixes":[]},{"propositions":[],"lastnames":["Cuggia"],"firstnames":["Marc"],"suffixes":[]}],"month":"August","year":"2019","pmid":"31437882","keywords":"Computing Methodologies, Drug Interaction, Machine Learning","pages":"45–49","bibtex":"@article{bouzille_automated_2019,\n\ttitle = {An {Automated} {Detection} {System} of {Drug}-{Drug} {Interactions} from {Electronic} {Patient} {Records} {Using} {Big} {Data} {Analytics}},\n\tvolume = {264},\n\tissn = {1879-8365},\n\tdoi = {10.3233/SHTI190180},\n\tabstract = {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.},\n\tlanguage = {eng},\n\tjournal = {Studies in Health Technology and Informatics},\n\tauthor = {Bouzillé, Guillaume and Morival, Camille and Westerlynck, Richard and Lemordant, Pierre and Chazard, Emmanuel and Lecorre, Pascal and Busnel, Yann and Cuggia, Marc},\n\tmonth = aug,\n\tyear = {2019},\n\tpmid = {31437882},\n\tkeywords = {Computing Methodologies, Drug Interaction, Machine Learning},\n\tpages = {45--49},\n}\n\n","author_short":["Bouzillé, G.","Morival, C.","Westerlynck, R.","Lemordant, P.","Chazard, E.","Lecorre, P.","Busnel, Y.","Cuggia, M."],"key":"bouzille_automated_2019","id":"bouzille_automated_2019","bibbaseid":"bouzill-morival-westerlynck-lemordant-chazard-lecorre-busnel-cuggia-anautomateddetectionsystemofdrugdruginteractionsfromelectronicpatientrecordsusingbigdataanalytics-2019","role":"author","urls":{},"keyword":["Computing Methodologies","Drug Interaction","Machine Learning"],"metadata":{"authorlinks":{"chazard, e":"http://www.chazard.org/emmanuel/publications.htm"}},"downloads":0},"bibtype":"article","biburl":"https://api.zotero.org/users/1597782/collections/MSB7W4UM/items?key=gxIPM4PJtMVcB8OpssCWodtP&format=bibtex&limit=100&start=0&sort=date","creationDate":"2019-09-06T17:16:28.221Z","downloads":0,"keywords":["computing methodologies","drug interaction","machine learning"],"search_terms":["automated","detection","system","drug","drug","interactions","electronic","patient","records","using","big","data","analytics","bouzillé","morival","westerlynck","lemordant","chazard","lecorre","busnel","cuggia"],"title":"An Automated Detection System of Drug-Drug Interactions from Electronic Patient Records Using Big Data Analytics","year":2019,"dataSources":["doevpoZ8x7wJceFTM"]}