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

ABSTRACT: 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 learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classifi

SUBMITTER: Ahmmed S 

PROVIDER: S-EPMC10685145 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature