Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.
School of Public Health, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Sensors (Basel). 2020 Oct 27;20(21):6100. doi: 10.3390/s20216100.
Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.
机器学习技术在当今的医疗保健领域被广泛用于疾病的诊断、预后和治疗。这些技术在造血细胞移植（HCT）领域有应用，HCT 是治疗血液系统恶性肿瘤的一种潜在的根治疗法。在此，我们对机器学习（ML）技术在 HCT 环境中的应用进行了系统评价。我们检查了所包含的数据类型、使用的特定 ML 技术以及测量的临床结果类型。我们使用 PubMed、Scopus、Web of Science 和 IEEE Xplore 数据库对英文文章进行了系统评价。搜索词包括“造血细胞移植（HCT）”、“自体 HCT”、“异基因 HCT”、“机器学习”和“人工智能”。仅纳入 2015 年 1 月至 2020 年 7 月期间发表的全文研究。两位作者使用预定义的数据字段提取数据。根据 PRISMA 指南，共确定了 242 项研究，其中 27 项研究符合纳入标准。这些研究分为三个广泛的主题，使用的 ML 技术类型包括集成学习（63%）、回归（44%）、贝叶斯学习（30%）和支持向量机（30%）。大多数研究都检查了用于预测 HCT 结果（例如，生存、复发、移植物抗宿主病）的模型。临床和遗传数据是建模过程中最常用的预测因子。总体而言，本综述提供了一个关于在 HCT 背景下应用 ML 技术的系统评价。证据还不够充分，无法确定在 HCT 环境中使用的最佳 ML 技术，以及需要哪些最小的数据变量。
