{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:08:34Z","timestamp":1773180514819,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH\/NHBLI","award":["1R01HL146354"],"award-info":[{"award-number":["1R01HL146354"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u201chematopoietic cell transplantation (HCT),\u201d \u201cautologous HCT,\u201d \u201callogeneic HCT,\u201d \u201cmachine learning,\u201d and \u201cartificial intelligence.\u201d 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.<\/jats:p>","DOI":"10.3390\/s20216100","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"6100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6221-4712","authenticated-orcid":false,"given":"Vibhuti","family":"Gupta","sequence":"first","affiliation":[{"name":"Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Thomas M.","family":"Braun","sequence":"additional","affiliation":[{"name":"School of Public Health, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0884-6740","authenticated-orcid":false,"given":"Mosharaf","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Michigan Engineering, Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Muneesh","family":"Tewari","sequence":"additional","affiliation":[{"name":"Michigan Medicine, Department of Internal Medicine, Hematology\/Oncology Division, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Michigan Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6321-3834","authenticated-orcid":false,"given":"Sung Won","family":"Choi","sequence":"additional","affiliation":[{"name":"Michigan Medicine, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"ref_1","unstructured":"Mitchell, T. 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