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

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.

Prevalence and nature of statin drug-drug interactions in a university hospital by electronic health record mining.

AIM: Our aim was to describe prevalence, nature, and level of severity of potential statin drug-drug interactions in a university hospital. METHODS: In a cross-sectional study, statin drug-drug interactions were screened from medical record of 10,506 in-patients treated stored in the clinical data warehouse "eHOP." We screened drug-drug interactions using Theriaque and Micromedex drug databases. RESULTS: A total of 22.5% of patients were exposed to at least one statin drug-drug interaction. Given their lipophilicity and CYP3A4 metabolic pathway, atorvastatin and simvastatin presented a higher prevalence of drug-drug interactions while fluvastatin presented the lowest prevalence. Up to 1% of the patients was exposed to a contraindicated drug-drug interaction, the most frequent drug-drug interaction involving influx-transporter (i.e., OATP1B1) interactions between simvastatin or rosuvastatin with cyclosporin. The second most frequent contraindicated drug-drug interaction involved CYP3A4 interaction between atorvastatin or simvastatin with either posaconazole or erythromycin. Furthermore, our analysis showed some discrepancies between Theriaque and Micromedex in the prevalence and the nature of drug-drug interactions. CONCLUSIONS: Different drug-drug interaction profiles were observed between statins with a higher prevalence of CYP3A4-based interactions for lipophilic statins. Analyzing the three most frequent DDIs, the more significant DDIs (level 1: contraindication) were reported for transporter-based DDI involving OATP1B1 influx transporter. These points are of concern to improve prescriptions of statins.

Sharing Health Big Data for Research - A Design by Use Cases: The INSHARE Platform Approach.

Sharing and exploiting Health Big Data (HBD) allow tackling challenges: data protection/governance taking into account legal, ethical, and deontological aspects enables trust, transparent and win-win relationship between researchers, citizens, and data providers. Lack of interoperability: compartmentalized and syntactically/semantica heterogeneous data. INSHARE project using experimental proof of concept explores how recent technologies overcome such issues. Using 6 data providers, platform is designed via 3 steps to: (1) analyze use cases, needs, and requirements; (2) define data sharing governance, secure access to platform; and (3) define platform specifications. Three use cases - from 5 studies and 11 data sources - were analyzed for platform design. Governance derived from SCANNER model was adapted to data sharing. Platform architecture integrates: data repository and hosting, semantic integration services, data processing, aggregate computing, data quality and integrity monitoring, Id linking, multisource query builder, visualization and data export services, data governance, study management service and security including data watermarking.