{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:22:20Z","timestamp":1773494540688,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Objective: Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems.<\/jats:p>\n               <jats:p>Design: Clinical condition\u2013syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions.<\/jats:p>\n               <jats:p>Results: Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created.<\/jats:p>\n               <jats:p>Limitations: The consensus definitions have not yet been validated through implementation.<\/jats:p>\n               <jats:p>Conclusion: The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.<\/jats:p>","DOI":"10.1136\/jamia.2010.003210","type":"journal-article","created":{"date-parts":[[2010,9,6]],"date-time":"2010-09-06T10:57:52Z","timestamp":1283770672000},"page":"595-601","source":"Crossref","is-referenced-by-count":24,"title":["Developing syndrome definitions based on consensus and current use"],"prefix":"10.1093","volume":"17","author":[{"given":"Wendy W","family":"Chapman","sequence":"first","affiliation":[{"name":"1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"}]},{"given":"John N","family":"Dowling","sequence":"additional","affiliation":[{"name":"1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"}]},{"given":"Atar","family":"Baer","sequence":"additional","affiliation":[{"name":"2Public Health \u2013 Seattle and King County, Seattle, USA"}]},{"given":"David L","family":"Buckeridge","sequence":"additional","affiliation":[{"name":"3McGill Clinical and Health Informatics, McGill University, Qu\u00e9bec, Canada"}]},{"given":"Dennis","family":"Cochrane","sequence":"additional","affiliation":[{"name":"4Emergency Medical Associates Research Foundation, Livingston, New Jersey, USA"}]},{"given":"Michael A","family":"Conway","sequence":"additional","affiliation":[{"name":"1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"}]},{"given":"Peter","family":"Elkin","sequence":"additional","affiliation":[{"name":"5Mount Sinai School of Medicine, New York, New York, USA"}]},{"given":"Jessi","family":"Espino","sequence":"additional","affiliation":[{"name":"6RODS Laboratory, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA"}]},{"given":"Julia E","family":"Gunn","sequence":"additional","affiliation":[{"name":"7Infectious Disease Bureau, Boston Public Health Commission, Boston, Massachusetts, USA"}]},{"given":"Craig M","family":"Hales","sequence":"additional","affiliation":[{"name":"8Centers for Disease Control and Prevention, Atlanta, Georgia, USA"}]},{"given":"Lori","family":"Hutwagner","sequence":"additional","affiliation":[{"name":"8Centers for Disease Control and Prevention, Atlanta, Georgia, USA"}]},{"given":"Mikaela","family":"Keller","sequence":"additional","affiliation":[{"name":"9Children's Hospital Boston, Boston, Massachusetts, USA"}]},{"given":"Catherine","family":"Larson","sequence":"additional","affiliation":[{"name":"10Management Information Systems Department, University of Arizona, Phoenix, Arizona, USA"}]},{"given":"Rebecca","family":"Noe","sequence":"additional","affiliation":[{"name":"11National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA"}]},{"given":"Anya","family":"Okhmatovskaia","sequence":"additional","affiliation":[{"name":"12Department of Epidemiology, Biostatistics and 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