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Machine learning in haematological malignancies.

Abstract：

:Machine learning is a branch of computer science and statistics that generates predictive or descriptive models by learning from training data rather than by being rigidly programmed. It has attracted substantial attention for its many applications in medicine, both as a catalyst for research and as a means of improving clinical care across the cycle of diagnosis, prognosis, and treatment of disease. These applications include the management of haematological malignancy, in which machine learning has created inroads in pathology, radiology, genomics, and the analysis of electronic health record data. As computational power becomes cheaper and the tools for implementing machine learning become increasingly democratised, it is likely to become increasingly integrated into the research and practice landscape of haematology. As such, machine learning merits understanding and attention from researchers and clinicians alike. This narrative Review describes important concepts in machine learning for unfamiliar readers, details machine learning's current applications in haematological malignancy, and summarises important concepts for clinicians to be aware of when appraising research that uses machine learning.

journal_name

Lancet Haematol

journal_title

The Lancet. Haematology

authors

Radakovich N,Nagy M,Nazha A

doi

10.1016/S2352-3026(20)30121-6

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

e541-e550

issue

7

issn

2352-3026

pii

S2352-3026(20)30121-6

journal_volume

7

pub_type

杂志文章,评审

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