Displaying results 1 - 3 of 3
-
Identifying Emerging Novel Outbreaks In Textual Emergency Department Data
Content Type: Abstract
Typical approaches to monitoring ED data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. However, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. To… read more… would not be detected by existing syndromes. Clusters may be based on symptoms, events, place names, arrival time, … The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data … The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data … -
Support Vector Subset Scan for Spatial Outbreak Detection
Content Type: Abstract
Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan… read more… ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The … ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The … -
StarScan: A Novel Scan Statistic for Irregularly-Shaped Spatial Clusters
Content Type: Abstract
Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets… read more… subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is … subset scanning within each circular neighborhood2, may not necessarily capture the pattern of interest, and is …