Machine learning in haematological malignancies. Issue 7 (July 2020)

• Record Type: Journal Article
• Authors: Radakovich, Nathan; Nagy, Matthew; Nazha, Aziz
• Is Part Of: Lancet. Volume 7:Issue 7(2020)
• Page Start: e541
• Page End: e550
• Publication Date: 2020-07
• Subjects: Hematology -- Periodicals; Blood -- Diseases -- Periodicals; 616.15005
• DOI: 10.1016/S2352-3026(20)30121-6
• Languages: English
• ISSNs: 2352-3026

Abstract

Summary: 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.