Understanding detection performance in public health surveillance: modeling aberrancy-detection algorithms.
Objective: Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 15; no. 6; pp. 760 - 770 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Nov/Dec2008
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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=105586741&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105586741 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Nov/Dec2008 vid: 15 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 105586741 NLM18755992 2010121393 10.1197/jamia.M2799 NLM18755992 PMC2585528 105586741 ppf: 760 ppct: 10 formats: tig: atl: Understanding detection performance in public health surveillance: modeling aberrancy-detection algorithms. aug: au: Buckeridge DL Okhmatovskaia A Tu S O'Connor M Nyulas C Musen MA Buckeridge, David L Okhmatovskaia, Anna Tu, Samson O'Connor, Martin Nyulas, Csongor Musen, Mark A affil: Department of Epidemiology and Biostatistics, McGill University, Montreal, Canada sug: subj: Detection Algorithms Population Surveillance Methods Disease Outbreaks Models, Theoretical Human Funding Source ab: Objective: Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative.Design: We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner.Measurement: We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms.Results: Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results.Conclusion: The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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