Adult Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Br J Haematol. 2021 Jan;192(2):239-250. doi: 10.1111/bjh.16915. Epub 2020 Jun 30.
Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine-learning studies are provided. Finally, we discuss limitations of the machine-learning approach.
数字化的医疗记录和基因组方法整合到临床实践中产生了前所未有的大量数据。机器学习是人工智能的一个分支，试图从复杂的数据结构中计算提取有意义的见解。机器学习在血液学场景中的应用正在稳步增加。然而，基本概念对临床医生和研究人员来说往往并不熟悉。本文的目的是为读者提供工具来解释和批判性地评估机器学习文献。我们首先阐明标准术语，然后回顾血液学中的示例。提供了设计和评估机器学习研究的指南。最后，我们讨论了机器学习方法的局限性。
