TY - JOUR AU - Colón-González, Felipe J. AU - Lake, Iain AU - Barker, Gary AU - Smith, Gillian E. AU - Elliot, Alex J. AU - Morbey, Roger PY - 2016/03/24 Y2 - 2024/03/28 TI - Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance JF - Online Journal of Public Health Informatics JA - OJPHI VL - 8 IS - 1 SE - Oral Presentations DO - 10.5210/ojphi.v8i1.6415 UR - https://ojphi.org/ojs/index.php/ojphi/article/view/6415 SP - AB - <p class="p1">The decision as to whether an alarm (excess activity in syndromic surveillance indicators) leads to an alert (a public health response) is often based on expert knowledge. Expert-based approaches may produce faster results than automated approaches but could be difficult to replicate. Moreover, the effectiveness of a syndromic surveillance system could be compromised in the absence of such experts. Bayesian network structural learning provides a mechanism to identify and represent relations between syndromic indicators, and between these indicators and alerts. Their outputs have the potential to assist decision-makers determine more effectively which alarms are most likely to lead to alerts.</p> ER -