A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine lea...

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Vydané v:	Heliyon Ročník 9; číslo 11; s. e21523
Hlavní autori:	Ahmmed, Syed, Mondal, M. Rubaiyat Hossain, Mia, Md Raihan, Adibuzzaman, Mohammad, Hoque, Abu Sayed Md. Latiful, Ahamed, Sheikh Iqbal
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.11.2023 Elsevier
Predmet:	algorithms Bangladesh biomedical research data collection Data quality Data science Electronic health records laboratory experimentation LOINC Machine learning SNOMED CT Standardization String distance similarity Bangladesh SNOMED CT Standardization Machine learning LOINC Data science String distance similarity Data quality Electronic health records
ISSN:	2405-8440, 2405-8440
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