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

Cao, Yuan; Cheng, Ming Fang; Hu, Chengjin

doi:10.1515/cclm-2012-0272

Cited by 14 publications

(1 citation statement)

References 24 publications

(29 reference statements)

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“…Yuan et al exploited three classifiers based on supervised ML methods to distinguish between positive and negative urine samples. Based on a classification and regression tree (CART), the model made predictions about the test results with 95.6% overall sensitivity [20]. The results showed that ML is a valuable method for creating classifiers for urine microscopic examination rules.…”

Section: -1-literature Surveymentioning

confidence: 99%

A Machine Learning Approach to Predict Creatine Kinase Test Results

et al. 2020

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Most of the research done in the literature are based on statistical approaches and used for deriving reference limits based on lab results. As more data are available to the researchers, ML methods are more effectively used by the clinicians and practitioners to reduce cost and provide more accurate diagnoses. This study aims to contribute to the medical laboratory processes by providing an automated method in order to predict the lab results accurately by machine learning from the previous test results. All patient data obtained have been anonymized, and a total of 449,471 test results have been used to build an integrated dataset. A total of 107,646 unique patients’ data has been used. This study aims to predict the value range of the Creatine Kinase tests, which are taken in separate tubes and usually needs more processing time than the other tests do. Using the lab results and the Random Forest Algorithm, this study reports that the outcome of the Creatine Kinase test can be determined with 97% accuracy by using the AST and ALT test values. This is an important achievement for the practitioners and the patients, as this study submits significant reduction in Creating Kinase test evaluation time.

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Section: -1-literature Surveymentioning

confidence: 99%

A Machine Learning Approach to Predict Creatine Kinase Test Results

et al. 2020

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Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

Xie,

Jia,

Liu

2024

Interdisciplinary Medicine

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Artificial intelligence (AI)‐driven analysis of comprehensive clinical parameters is bringing about a significant transformation in traditional routine clinical laboratory testing. This transformation impacts the prediction, prevention, diagnosis, and prognosis of human diseases. AI possesses the capability to efficiently analyze and process vast and intricate datasets, thereby facilitating the development of diverse and efficient diagnostic or predictive models. This advancement is fueling significant improvements in laboratory quality, automation, and the accuracy of diagnoses. In this context, we conducted a thorough review and discussion on the progression of AI applications in clinical laboratory medicine, encompassing advancements, implementation, and challenges. Our conclusion underscores that integrating AI into clinical laboratory testing will notably propel personalized precision medicine forward and enhance diagnostic accuracy, especially benefiting patients for whom accurate diagnoses are elusive through traditional laboratory testing systems.

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Comparison of the Automated cobas u 701 Urine Microscopy and UF‐1000i Flow Cytometry Systems and Manual Microscopy in the Examination of Urine Sediments

Lee

Ryoo

2016

Clinical Laboratory Analysis

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

Abstract: 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.

Cited by 14 publications

References 24 publications

A Machine Learning Approach to Predict Creatine Kinase Test Results

A Machine Learning Approach to Predict Creatine Kinase Test Results

Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

Comparison of the Automated cobas u 701 Urine Microscopy and UF‐1000i Flow Cytometry Systems and Manual Microscopy in the Examination of Urine Sediments

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