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Review
. 2020 Dec 21;3(1):13-20.
doi: 10.2991/chi.k.201130.001. eCollection 2021 Mar.

Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases

Ibrahim N Muhsen  1 , David Shyr  2 , Anthony D Sung  3 , Shahrukh K Hashmi  4   5
Affiliations
Review

Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases

Ibrahim N Muhsen et al. Clin Hematol Int. .

Abstract

The use of machine learning (ML) and deep learning (DL) methods in hematology includes diagnostic, prognostic, and therapeutic applications. This increase is due to the improved access to ML and DL tools and the expansion of medical data. The utilization of ML remains limited in clinical practice, with some disciplines further along in their adoption, such as radiology and histopathology. In this review, we discuss the current uses of ML in diagnosis in the field of hematology, including image-recognition, laboratory, and genomics-based diagnosis. Additionally, we provide an introduction to the fields of ML and DL, highlighting current trends, limitations, and possible areas of improvement.

Keywords: Hematology; artificial intelligence; machine learning.

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Conflict of interest statement

The authors declare they have no conflicts of interest.

Figures

Figure 1
Figure 1
Supervised machine learning and its different subtypes.
Figure 2
Figure 2
Deep learning with description of two types of neural networks frequently used in medical literature.
Figure 3
Figure 3
Confusion matrix.
See this image and copyright information in PMC

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References

    1. Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46:5–17. doi: 10.1016/j.artmed.2008.07.017. - DOI - PMC - PubMed
    1. Kantarjian H, Yu PP. Artificial intelligence, big data, and cancer. JAMA Oncol. 2015;1:573–4. doi: 10.1001/jamaoncol.2015.1203. - DOI - PubMed
    1. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319:1317–18. doi: 10.1001/jama.2017.18391. - DOI - PubMed
    1. Topol EJ. High‐performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. doi: 10.1038/s41591-018-0300-7. - DOI - PubMed
    1. Char D, Shah N, Magnus D. Implementing machine learning in health care — addressing ethical challenges. N Engl J Med. 2018;378:981–3. doi: 10.1056/NEJMp1714229. - DOI - PMC - PubMed

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