Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

<h4>Background</h4>Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated clas...

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Autores principales:	Fernanda C Dórea, C Anne Muckle, David Kelton, J T McClure, Beverly J McEwen, W Bruce McNab, Javier Sanchez, Crawford W Revie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3677b33908a64b5ba6dedea7bc5da9ce
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spelling	oai:doaj.org-article:3677b33908a64b5ba6dedea7bc5da9ce2021-11-18T07:54:28ZExploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.1932-620310.1371/journal.pone.0057334https://doaj.org/article/3677b33908a64b5ba6dedea7bc5da9ce2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23505427/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users.<h4>Methods</h4>This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory.<h4>Results</h4>High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F(1-micro) score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F(1-macro)), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F(1-micro) score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F(1-micro)= 0.994 and F(1-macro) = .955), however the classification process is not transparent to the domain experts.<h4>Conclusion</h4>The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.Fernanda C DóreaC Anne MuckleDavid KeltonJ T McClureBeverly J McEwenW Bruce McNabJavier SanchezCrawford W ReviePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e57334 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fernanda C Dórea C Anne Muckle David Kelton J T McClure Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
description	<h4>Background</h4>Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users.<h4>Methods</h4>This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory.<h4>Results</h4>High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F(1-micro) score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F(1-macro)), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F(1-micro) score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F(1-micro)= 0.994 and F(1-macro) = .955), however the classification process is not transparent to the domain experts.<h4>Conclusion</h4>The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input.
format	article
author	Fernanda C Dórea C Anne Muckle David Kelton J T McClure Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie
author_facet	Fernanda C Dórea C Anne Muckle David Kelton J T McClure Beverly J McEwen W Bruce McNab Javier Sanchez Crawford W Revie
author_sort	Fernanda C Dórea
title	Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
title_short	Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
title_full	Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
title_fullStr	Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
title_full_unstemmed	Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
title_sort	exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/3677b33908a64b5ba6dedea7bc5da9ce
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Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

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