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

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, w...

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Detalles Bibliográficos
Autores principales: Muhsen, Ibrahim N., Shyr, David, Sung, Anthony D., Hashmi, Shahrukh K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Atlantis Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432325/
https://www.ncbi.nlm.nih.gov/pubmed/34595462
http://dx.doi.org/10.2991/chi.k.201130.001
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author Muhsen, Ibrahim N.
Shyr, David
Sung, Anthony D.
Hashmi, Shahrukh K.
author_facet Muhsen, Ibrahim N.
Shyr, David
Sung, Anthony D.
Hashmi, Shahrukh K.
author_sort Muhsen, Ibrahim N.
collection PubMed
description 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.
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spelling pubmed-84323252021-09-29 Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases Muhsen, Ibrahim N. Shyr, David Sung, Anthony D. Hashmi, Shahrukh K. Clin Hematol Int Review 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. Atlantis Press 2020-12-21 /pmc/articles/PMC8432325/ /pubmed/34595462 http://dx.doi.org/10.2991/chi.k.201130.001 Text en © 2020 International Academy for Clinical Hematology. Publishing services by Atlantis Press International B.V. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ).
spellingShingle Review
Muhsen, Ibrahim N.
Shyr, David
Sung, Anthony D.
Hashmi, Shahrukh K.
Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title_full Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title_fullStr Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title_full_unstemmed Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title_short Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases
title_sort machine learning applications in the diagnosis of benign and malignant hematological diseases
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8432325/
https://www.ncbi.nlm.nih.gov/pubmed/34595462
http://dx.doi.org/10.2991/chi.k.201130.001
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