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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Auteurs principaux: | 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 |
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Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2019
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Sujets: | |
Accès en ligne: | https://doaj.org/article/3428a47f1e9049b480633dbccf66043a |
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