Department of Clinical Informatics, Lucile Packard Children's Hospital, Palo Alto, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.
Clin Biochem. 2022 May;103:1-7. doi: 10.1016/j.clinbiochem.2022.02.011. Epub 2022 Feb 25.
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.
机器学习能够利用大量数据来推断复杂的模式，而这些模式是基于规则的系统和人类专家所无法企及的。它在医学实验室中的应用尤其令人兴奋，因为实验室检测为临床决策提供了重要的基础。在本文中，我们除了提供全面的文献综述，概述机器学习在常规实验室医学中的应用现状外，还为医学专业人员简要介绍了机器学习。尽管机器学习仍处于早期阶段，但它已被用于自动化实验室任务、优化利用率，并提供个性化的参考范围和检测解释。已发表的文献使我们相信，机器学习将成为实验室从业者越来越重要的领域。我们可以预见，未来的实验室将利用这些方法来显著提高效率和诊断精度。
