机器学习在良恶性血液病诊断中的应用。

Clinical Hematology International Pub Date : 2020-12-21 eCollection Date: 2021-03-01 DOI:10.2991/chi.k.201130.001
Ibrahim N Muhsen, David Shyr, Anthony D Sung, Shahrukh K Hashmi
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引用次数: 9

摘要

机器学习(ML)和深度学习(DL)方法在血液学中的应用包括诊断、预后和治疗应用。这一增长是由于对ML和DL工具的访问改进以及医疗数据的扩展。ML在临床实践中的应用仍然有限,一些学科在其采用方面进一步发展,如放射学和组织病理学。在这篇综述中,我们讨论了目前机器学习在血液学诊断领域的应用,包括图像识别、实验室和基于基因组学的诊断。此外,我们还介绍了机器学习和深度学习领域,强调了当前的趋势、局限性和可能的改进领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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, 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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