A data-driven method to detect adverse drug events from prescription data.
Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are nee...
| Published in: | Journal of Biomedical Informatics Vol. 85; pp. 10 - 21 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Sep2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131732867&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131732867 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Sep2018 vid: 85 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 131732867 131732867 NLM30016721 131732867 10.1016/j.jbi.2018.07.013 NLM30016721 131732867 ppf: 10 ppct: 11 formats: tig: atl: A data-driven method to detect adverse drug events from prescription data. aug: au: Zhan, Chen Roughead, Elizabeth Liu, Lin Pratt, Nicole Li, Jiuyong affil: School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Adelaide, South Australia 5095, Australia sug: subj: Adverse Drug Event Bioinformatics Databases Australia Human Pilot Studies Data Mining Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈Dp→Ds〉, the prior medicine Dp leads to an ADE for which the succeeding medicine Ds is dispensed to treat. We propose a data-driven method which integrates (1) a constrained sequential pattern mining to uncover prescription sequences as potential signals of ADEs, (2) domain constraints to eliminate interference signals and (3) an adapted Self-Controlled Case Series model to evaluate the potential signals of ADEs. Despite ample prior works using Electronic Health Records (EHRs), our method utilises pure prescription data which does not contain additional information, e.g. symptoms or diagnoses as included in EHRs. To assess the performance of the proposed method, we apply it to a real-world dataset from the Pharmaceutical Benefits Scheme of Australia. The dataset contains over 50 million records covering approximately 2 million patients. The results demonstrate the effectiveness of our method in identifying both known ADEs and unknown yet suspicious ADEs with limited detection of false positive signals. Comparing to a recognised gold standard, our method successfully detects 67.4% of the positive adverse events while only 8.78% false positives exist. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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