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

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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Bibliographic Details
Main Authors: Syed Ahmmed, M. Rubaiyat Hossain Mondal, Md Raihan Mia, Mohammad Adibuzzaman, Abu Sayed Md. Latiful Hoque, Sheikh Iqbal Ahamed
Format: Article
Language:English
Published: Elsevier 2023-11-01
Series:Heliyon
Subjects:
Standardization
Electronic health records
LOINC
SNOMED CT
Machine learning
String distance similarity
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844023087315

Internet

http://www.sciencedirect.com/science/article/pii/S2405844023087315

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