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A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

1
Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
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School of Public Health, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
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Michigan Engineering, Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Michigan Medicine, Department of Internal Medicine, Hematology/Oncology Division, University of Michigan, Ann Arbor, MI 48109, USA
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Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Michigan Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(21), 6100; https://doi.org/10.3390/s20216100
Received: 16 September 2020 / Revised: 19 October 2020 / Accepted: 25 October 2020 / Published: 27 October 2020
(This article belongs to the Special Issue mHealth Platform and Sensors)
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. View Full-Text
Keywords: machine learning; artificial intelligence; sensors; mobile health; mHealth; hematopoietic stem cell transplantation; HSCT machine learning; artificial intelligence; sensors; mobile health; mHealth; hematopoietic stem cell transplantation; HSCT
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MDPI and ACS Style

Gupta, V.; Braun, T.M.; Chowdhury, M.; Tewari, M.; Choi, S.W. A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT). Sensors 2020, 20, 6100. https://doi.org/10.3390/s20216100

AMA Style

Gupta V, Braun TM, Chowdhury M, Tewari M, Choi SW. A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT). Sensors. 2020; 20(21):6100. https://doi.org/10.3390/s20216100

Chicago/Turabian Style

Gupta, Vibhuti; Braun, Thomas M.; Chowdhury, Mosharaf; Tewari, Muneesh; Choi, Sung W. 2020. "A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)" Sensors 20, no. 21: 6100. https://doi.org/10.3390/s20216100

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