A Multiagent System for Integrated Detection of Pharmacovigilance Signals.
Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called 'signals', i.e. potential associations between drugs and adverse ef...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 2; pp. 1 - 15 |
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| Autores principales: | , |
| Formato: | equations & formulas review Journal Article |
| Publicado: |
Springer Nature
Feb2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111115366&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111115366 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2016 vid: 40 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111115366 111115366 111115366 10.1007/s10916-015-0378-0 111115366 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A Multiagent System for Integrated Detection of Pharmacovigilance Signals. aug: au: Koutkias, Vassilis Jaulent, Marie-Christine sug: subj: Pharmacovigilance Methods Drug Evaluation Safety Signal Processing, Computer Assisted Data Analysis Evaluation Marketing Adverse Drug Event ab: Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called 'signals', i.e. potential associations between drugs and adverse effects, that are unknown or incompletely documented. Various computational methods have been proposed to support domain experts in signal detection. However, recent comparative studies illustrated that current methods exhibit high false-positive rates, significantly variable performance across different datasets used for analysis and events of interest, but also complementarity in their outcomes. In this regard, in order to reinforce accurate and timely signal detection, we elaborated through an agent-based approach towards systematic, joint exploitation of multiple heterogeneous signal detection methods, data sources and other drug-related resources under a common, integrated framework. The approach relies on a multiagent system operating based on a collaborative agent interaction protocol, aiming to implement a comprehensive workflow that comprises of method selection and execution, as well as outcomes' aggregation, filtering, ranking and annotation. This paper presents the design of the proposed multiagent system, discusses implementation issues and demonstrates the applicability of the proposed solution in an example signal detection scenario. This work constitutes a step towards large-scale, integrated and knowledge-intensive computational signal detection. pubtype: Academic Journal doctype: equations & formulas review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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