Ronzio Luca, Cabitza Federico, Barbaro Alessandro, Banfi Giuseppe
Department of Informatics, University of Milano-Bicocca, 20126 Milan, Italy.
IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161 Milan, Italy.
Diagnostics (Basel). 2021 Feb 22;11(2):372. doi: 10.3390/diagnostics11020372.
This article presents a systematic literature review that expands and updates a previous review on the application of machine learning to laboratory medicine. We used Scopus and PubMed to collect, select and analyse the papers published from 2017 to the present in order to highlight the main studies that have applied machine learning techniques to haematochemical parameters and to review their diagnostic and prognostic performance. In doing so, we aim to address the question we asked three years ago about the potential of these techniques in laboratory medicine and the need to leverage a tool that was still under-utilised at that time.
本文呈现了一项系统性文献综述,该综述扩展并更新了之前关于机器学习在检验医学中应用的综述。我们使用Scopus和PubMed数据库来收集、筛选和分析2017年至今发表的论文,以突出那些将机器学习技术应用于血液生化参数的主要研究,并评估其诊断和预后性能。通过这样做,我们旨在回答三年前提出的关于这些技术在检验医学中的潜力以及利用当时仍未得到充分利用的工具的必要性的问题。