Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

Abstract Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hos...

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Autores principales:	Hsin-Yao Wang, Chung-Chih Hung, Chun-Hsien Chen, Tzong-Yi Lee, Kai-Yao Huang, Hsiao-Chen Ning, Nan-Chang Lai, Ming-Hsiu Tsai, Li-Chuan Lu, Yi-Ju Tseng, Jang-Jih Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3428a47f1e9049b480633dbccf66043a
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spelling	oai:doaj.org-article:3428a47f1e9049b480633dbccf66043a2021-12-02T15:09:45ZIncrease Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach10.1038/s41598-019-47361-82045-2322https://doaj.org/article/3428a47f1e9049b480633dbccf66043a2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-47361-8https://doaj.org/toc/2045-2322Abstract Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.Hsin-Yao WangChung-Chih HungChun-Hsien ChenTzong-Yi LeeKai-Yao HuangHsiao-Chen NingNan-Chang LaiMing-Hsiu TsaiLi-Chuan LuYi-Ju TsengJang-Jih LuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hsin-Yao Wang Chung-Chih Hung Chun-Hsien Chen Tzong-Yi Lee Kai-Yao Huang Hsiao-Chen Ning Nan-Chang Lai Ming-Hsiu Tsai Li-Chuan Lu Yi-Ju Tseng Jang-Jih Lu Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
description	Abstract Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.
format	article
author	Hsin-Yao Wang Chung-Chih Hung Chun-Hsien Chen Tzong-Yi Lee Kai-Yao Huang Hsiao-Chen Ning Nan-Chang Lai Ming-Hsiu Tsai Li-Chuan Lu Yi-Ju Tseng Jang-Jih Lu
author_facet	Hsin-Yao Wang Chung-Chih Hung Chun-Hsien Chen Tzong-Yi Lee Kai-Yao Huang Hsiao-Chen Ning Nan-Chang Lai Ming-Hsiu Tsai Li-Chuan Lu Yi-Ju Tseng Jang-Jih Lu
author_sort	Hsin-Yao Wang
title	Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
title_short	Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
title_full	Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
title_fullStr	Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
title_full_unstemmed	Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
title_sort	increase trichomonas vaginalis detection based on urine routine analysis through a machine learning approach
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/3428a47f1e9049b480633dbccf66043a
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Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

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