A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).
A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).
复制标题
DOI：
10.3390/s20216100
复制
发表时间：
2020-10-27
期刊：
影响因子：
--
通讯作者：
Choi SW
中科院分区：
文献类型：
--
作者：
Gupta V;Braun TM;Chowdhury M;Tewari M;Choi SW
如今，机器学习技术被广泛应用于医疗保健领域，用于疾病的诊断、预测和治疗。这些技术在造血细胞移植(HCT)领域有应用，这是一种潜在的治疗血液系统恶性肿瘤的方法。在此，对机器学习(ML)技术在HCT环境中的应用进行了系统的回顾。我们检查了纳入的数据流类型、使用的特定ML技术以及测量的临床结果类型。使用PubMed、Scope us、Web of Science和IEEE Xplore数据库对英文文章进行了系统的综述。搜索关键词包括“造血细胞移植”、“自体造血干细胞移植”、“异基因造血干细胞移植”、“机器学习”和“人工智能”。只收录了2015年1月至2020年7月期间报告的全文研究。数据由两位作者使用预定义的数据字段提取。根据PRISMA指南，总共确定了242项研究，其中27项研究符合纳入标准。这些研究被细分为三大主题，所使用的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%)。大多数研究检查了预测HCT结果的模型(例如，存活率、复发、移植物抗宿主病)。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，本综述对应用于母婴同种异体移植的ML技术进行了系统的回顾。证据不足以确定在HCT设置中使用的最佳ML技术和/或需要的最小数据变量。
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.
登录
查看更多内容
影响因子：
4.8
作者：
Marino, S. R.;Lee, S. M.;Binkowski, T. A.;Wang, T.;Haagenson, M.;Wang, H-L;Maiers, M.;Spellman, S.;van Besien, K.;Lee, S. J.;Karrison, T.;Artz, A.
通讯作者：
Artz, A.
影响因子：
3.7
作者：
Brasier, Allan R.;Zhao, Yingxin;Wingard, John R.
通讯作者：
Wingard, John R.
影响因子：
4.7
作者：
Goswami, Chitrita;Poonia, Sarita;Sengupta, Debarka
通讯作者：
Sengupta, Debarka
影响因子：
5
作者：
Krakow, Elizabeth F.;Hemmer, Michael;Moodie, Erica E. M.
通讯作者：
Moodie, Erica E. M.
影响因子：
3
作者：
Leclerc V;Ducher M;Bleyzac N
通讯作者：
Bleyzac N