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• W2967141107 abstract "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." @default.
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• W2967141107 date "2019-08-19" @default.
• W2967141107 modified "2023-10-17" @default.
• W2967141107 title "Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach" @default.
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• W2967141107 doi "https://doi.org/10.1038/s41598-019-47361-8" @default.
• W2967141107 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6698480" @default.
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