Abstract

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

摘要

机器学习 (ML) 对临床检验医学的兴趣越来越大，主要是由于使用实验室自动化和计算能力生成和存储数据的成本降低，以及开源工具的广泛可访问性。尽管如此，目前只有少数基于 ML 的产品可用于常规临床实验室实践。在这篇综述中，我们首先介绍了 ML，首先概述了 ML 的格局、其一般工作流程以及临床实验室应用程序中最常用的算法。此外，我们旨在说明临床实验室环境中使用的技术的最新演变（2018 年至 2020 年中期），并讨论相关的挑战和机遇。在临床化学领域，ML 算法的审查应用包括实验室结果的质量审查、自动尿沉渣分析、根据常规实验室参数预测疾病或结果，以及复杂生化数据的解释。在血液学子学科中，我们讨论了自动血涂片报告和疟疾诊断的概念。最后，我们处理广泛的临床微生物学应用，例如通过实验室自动化减少诊断工作量、临床相关微生物的检测和鉴定以及抗菌素耐药性的检测。我们讨论了自动血涂片报告和疟疾诊断的概念。最后，我们处理广泛的临床微生物学应用，例如通过实验室自动化减少诊断工作量、临床相关微生物的检测和鉴定以及抗菌素耐药性的检测。我们讨论了自动血涂片报告和疟疾诊断的概念。最后，我们处理广泛的临床微生物学应用，例如通过实验室自动化减少诊断工作量、临床相关微生物的检测和鉴定以及抗菌素耐药性的检测。