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Automated detection of adverse drug events by data mining of electronic health records

Abstract : Adverse Drug Events (ADE) are injuries due to medication management rather than the underlying condition of the patient. They endanger the patients and most of them could be avoided. The detection of ADEs usually relies on spontaneous reporting ormedical chart reviews. The objective of the present work is to automatically detectcases of ADEs by means of Data Mining, which are a set of statistical methodsparticularly suitable for the discovery of rules in large datasets.MaterialA common data model is first defined to describe the available data extracted from the EHRs (electronic health records). More than 90,000 complete hospital stays areextracted from 5 French and Danish hospitals. Those complete records includediagnoses, lab results, drug administrations, administrative and demographic data aswell as free-text reports. When the drugs are not available from any CPOE(Computerized Prescription Order Entry), they are extracted from the free-text reports by means of semantic mining. In addition, an exhaustive set of SPCs (Summaries of Product Characteristics) is provided by the Vidal Company.MethodsWe attempt to trace all the outcomes that are described in the SPCs in the dataset. By means of data mining, especially Decision Trees and Association Rules, the patternsof conditions that participate in the occurrence of ADEs are identified. Many ADEdetection rules are generated; they are filtered and validated by an expert committee. Finally, the rules are described by means of XML files in a central rules repository, and are executed again for statistics computation and ADE detection.Results236 ADE-detection rules have been discovered. Those rules enable to detect 27different kinds of outcomes. Several statistics are automatically computed for eachrule in every medical department, such as the confidence or the relative risk. Thoserules involve innovative conditions: for instance some of them describe theconsequences of drug discontinuations.In addition, two web tools are designed and are available through the web for thephysicians of the departments: the Scorecards enable to display statistical andepidemiological information about ADEs in a given department and the ExpertExplorer enables the physicians to review the potential ADE cases of theirdepartment.Finally, a preliminary evaluation of the clinical impact of the potential ADEs isperformed as well as a preliminary evaluation of the accuracy of the ADE detection.
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Emmanuel Chazard. Automated detection of adverse drug events by data mining of electronic health records. Human health and pathology. Université du Droit et de la Santé - Lille II, 2011. English. ⟨NNT : 2011LIL2S009⟩. ⟨tel-00637254⟩

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