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Machine learning and artificial intelligence in haematology.
Br J Haematol. 2020 Jun 30 [Online ahead of print]BJ

Abstract

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.

Authors+Show Affiliations

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.University of Connecticut Medical Center, Farmington, CT, USA.Division of Hematology-Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.European Society for Blood and Marrow Transplantation Paris Study Office/CEREST-TC, Paris, France. Service d'Hématologie Clinique et de Thérapie Cellulaire, Hôpital Saint Antoine, AP-HP, Paris, France.Hematology and Bone Marrow Transplantation Division, Chaim Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

32602593

Citation

Shouval, Roni, et al. "Machine Learning and Artificial Intelligence in Haematology." British Journal of Haematology, 2020.
Shouval R, Fein JA, Savani B, et al. Machine learning and artificial intelligence in haematology. Br J Haematol. 2020.
Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2020). Machine learning and artificial intelligence in haematology. British Journal of Haematology. https://doi.org/10.1111/bjh.16915
Shouval R, et al. Machine Learning and Artificial Intelligence in Haematology. Br J Haematol. 2020 Jun 30; PubMed PMID: 32602593.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Machine learning and artificial intelligence in haematology. AU - Shouval,Roni, AU - Fein,Joshua A, AU - Savani,Bipin, AU - Mohty,Mohamad, AU - Nagler,Arnon, Y1 - 2020/06/30/ PY - 2020/7/1/entrez KW - artificial intelligence KW - haematology KW - leukaemia KW - machine learning KW - prediction models JF - British journal of haematology JO - Br. J. Haematol. N2 - 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. SN - 1365-2141 UR - https://www.unboundmedicine.com/medline/citation/32602593/Machine_learning_and_artificial_intelligence_in_haematology L2 - https://doi.org/10.1111/bjh.16915 DB - PRIME DP - Unbound Medicine ER -
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