Classification tree methods for development of decision rules for botulism and cyanide poisoning View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-06

AUTHORS

Howell Sasser, Marcy Nussbaum, Michael Beuhler, Marsha Ford

ABSTRACT

INTRODUCTION: Identification of predictors of potential mass poisonings may increase the speed and accuracy with which patients are recognized, potentially reducing the number ultimately exposed and the degree to which they are affected. This analysis used a decision-tree method to sort such potential predictors. METHODS: Data from the Toxic Exposure Surveillance System were used to select cyanide and botulism cases from 1993 to 2005 for analysis. Cases of other poisonings from a single poison center were used as controls. After duplication was omitted and removal of cases from the control sample was completed, there remained 1,122 cyanide cases, 262 botulism cases, and 70,804 controls available for both analyses. Classification trees for each poisoning type were constructed, using 131 standardized clinical effects. These decision rules were compared with the current case surveillance definitions of one active poison center and the American Association of Poison Control Centers (AAPCC). RESULTS: The botulism analysis produced a 4-item decision rule with sensitivity (Se) of 68% and specificity (Sp) of 90%. Use of the single poison center and AAPCC definitions produced Se of 19.5% and 16.8%, and Sp of 99.5% and 83.2%, respectively. The cyanide analysis produced a 9-item decision rule with Se of 74% and Sp of 77%. The single poison center and AAPCC case definitions produced Se of 10.2% and 8.6%, and Sp of 99.8% and 99.8%, respectively. CONCLUSIONS: These results suggest the possibility of improved poisoning case surveillance sensitivity using classification trees. This method produced substantially higher sensitivities, but not specificities, for both cyanide and botulism. Despite limitations, these results show the potential of a classification-tree approach in the detection of poisoning events. More... »

PAGES

77-83

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf03160959

DOI

http://dx.doi.org/10.1007/bf03160959

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031362071

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18570166


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