Clinical applications of machine learning in pre-analytical, analytical and post-analytical phases of laboratory medicine: a narrative review

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Abstract

Background and Objective: In an era of big data, various machine learning (ML) algorithms have increasingly been applied in clinical medicine. In particular, ML algorithms have greatly impacted the current concepts and developmental models of laboratory medicine. This narrative review aimed to address the clinical applications of ML algorithms in the pre-analytical, analytical, and post-analytical phases of laboratory medicine. Methods: PubMed database were searched to identify studies published between the years 2000/1/1– 2022/8/1. Articles investigating the clinical applications of ML algorithms in laboratory medicine were included. With typical examples, the clinical applications of ML algorithms were summarized in the pre-analytical, analytical, and post-analytical phases of laboratory medicine. Key Content and Findings: In the pre-analytical phase, ML algorithms can be used to reduce the rate of pre-analytical error and verify specimens with low quality. In the analytical phase, ML algorithms can be used to reduce laboratory costs and optimize laboratory work procedures. In the post-analytical phase, ML algorithms can be used to integrate the existing test results to guide the diagnosis and treatment of diseases. Conclusions: ML is a strong driver for the development of laboratory medicine.

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Zhang, L., & Hu, Z. D. (2022, December 30). Clinical applications of machine learning in pre-analytical, analytical and post-analytical phases of laboratory medicine: a narrative review. AME Medical Journal. AME Publishing Company. https://doi.org/10.21037/amj-22-92

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