Displaying results 769 - 776 of 826
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Using cKASS to facilitate knowledge authoring and sharing for syndromic surveillance
Content Type: Abstract
Mining text for real-time syndromic surveillance usually requires a comprehensive knowledge base (KB) which contains detailed information about concepts relevant to the domain, such as disease names, symptoms, drugs, and radiology findings. Two such… read more… modify and publish customized syndrome definitions con- structed from either publicly available or user-created … -
Predictors of vaccination uptake for the 2009 influenza pandemic (H1N1) in Montreal
Content Type: Abstract
Work on vaccination timing and promotion largely precedes the 2009 pandemic. Post-pandemic studies examining the wide range of local vaccination efforts mostly have been limited to surveys assessing the role of administrative strategies, logistical… read more… Rainfall�20 mm (ref: B20 mm) 0.67 [0.56�0.79] Snowfall�20 cm (ref: B20 cm) 0.43 [0.33�0.56] Weekend (ref: weekday) 1.02 [0.97�1.07] … -
Adverse Drug Events: Insights From Google Search Volume
Content Type: Abstract
Adverse drug events (ADEs) are a major cause of morbidity and mortality. However, post-marketing surveillance systems are passive and reporting is generally not mandated. Thus, many ADEs go unreported, and it is difficult to estimate and/or… read more… 3. Psaty BM, Korn D. Congress responds to the IOM drug safety report-in full. JAMA. 2007;298:2185�7. … -
Discovering the New Frontier of Syndromic Surveillance (Pt 2): A Meaningful Use Dialogue on the Department of Veterans Affairs Implementation
Content Type: Webinar
This webinar is part of the Meaningful Use Webinar Series entitled "Discovering the New Frontier of Syndromic Surveillance: A Meaningful Use Dialogue" Dr. Cynthia Lucero continues our Meaningful Use webinar series with an introduction to… read more… on board 6 Majority of existing ESSENCE data elements come from VistA (VA electronic medical record system) … -
November Literature Review: Epidemiology of influenza strains
Content Type: Webinar
Edward Goldstein, PhD, Harvard School of Public Health, Senior Research Scientist, Department of Epidemiology discusses his paper "Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method." Published in PLoS Med. 2011… read more… and Influenza mortality Base(t) = a ⋅sin( 2π t 52.2 )+ b ⋅cos( 2π t 52.2 )+ c + d ⋅ t + e ⋅ t2 +… • The coefficients … -
Syndromic Surveillance for Bicycle Related Injuries in Boston, 2007-2010
Content Type: Abstract
In May of 2001, Boston released a strategic transportation plan to improve bicycle access and safety. [1] According to the Boston Transportation Department, ridership has increased 122% between 2007 and 2009. [2] A collaborative public health and… read more… ZIP code of residence, chief complaints and ICD-9 CM-coded final diagnosis. Disposition information was … case definition that combined chief complaint and ICD-9 CM-coded information and excluded motor cycle only related … -
HL7 Version 2.5.1 PHIN Messaging Guide for Syndromic Surveillance: Emergency Department, Urgent Care and Inpatient Settings, Release 2.0, NIST Clarifications and Validation Guidelines, Version 1.5 (July 2016)
Content Type: Messaging Standard
This document lists conformance testing issues and associated policies derived by NIST, in collaboration with the CDC, based on a review of the HL7 Version 2.5.1 PHIN Messaging Guide for Syndromic Surveillance: Emergency Department, Urgent Care,… read more… support all 3 value sets for PV2-3 (Admit Reason): ICD-9 CM Administrative Diagnosis Codes; ICD-10 codes; SNOMED … -
Bayesian Surveillance for the Detection of Small Area Health Anomalies
Content Type: Webinar
The surveillance task when faced with small area health data is more complex than in the time domain alone. Both changes in time and space must be considered. Such questions as ‘where will the infection spread to next?’ and, ‘when will the infection… read more… l lj j ijkjkik ikikik w ePoy δ δρθ θ ),...,,( ,2,1, mjjjj wwww = ISDS WEBINAR JULY 28TH 2016 MULTIVARIATE SCENARIO: … )log( )log( )log( )log( )log( iiii iiii iii iii iiii ww ww w w ww ψδδρθ ψδδρθ ψδρθ ψδρθ ψδδρθ +++= +++= ++= ++= …