UrineCART, a machine learning method for establishment of review rules based on UF-1000i flow cytometry and dipstick or reflectance photometer.

Abstract:

BACKGROUND:Automated systems have been broadly used in the counting of particles in urine, while manual microscopic analyses are still required for confirming components of urine sediments, especially pathologic casts and other unknown particles. Good review rules can reduce the number of manual urine microscopy examinations safely, thereby increasing productivity. Although several methods have been proposed,establishment of microscopic review rules for fl ow cytometer remains challenging. METHODS:A total of 3014 urine samples from outpatient and inpatient were examined using UF-1000i flow cytometry,Urisys-2400 dipstick and RS 2003 urine sediment workstation,respectively. Based on the results above, three supervised machine learning methods were employed to construct classifiers for screening urine samples. RESULTS:Here, we propose a novel method for construction of microscopic review rules, termed UrineCART, which was based on a classification and regression tree (CART) method.With a cut-off value of 0.0745 for Urine CART, we obtained a sensitivity of 92.0 % , a specificity of 81.5 % and a total review rate of 32.4 % on an independent test set. Comparisons with the existing methods showed that Urine CART gave the acceptable sensitivity and lower total review rate. CONCLUSIONS:An algorithm based on machine learning methods for review criteria can be achieved via systematic comparison of UF-1000i flow cytometry and microscopy.Using Urine CART, our microscopic review rate can be reduced to around 30 % , while decreasing significant losses in urinalysis.

journal_name

Clin Chem Lab Med

authors

Cao Y,Cheng M,Hu C

doi

10.1515/cclm-2012-0272

subject

Has Abstract

pub_date

2012-12-01 00:00:00

pages

2155-61

issue

12

eissn

1434-6621

issn

1437-4331

pii

/j/cclm.ahead-of-print/cclm-2012-0272/cclm-2012-02

journal_volume

50

pub_type

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