Acute myeloid leukemia and artificial intelligence, algorithms and new scores.
Acute myeloid leukemia
Artificial intelligence
Genomics
Machine learning
Malignant hematology
Multi-omics
Risk stratification
Journal
Best practice & research. Clinical haematology
ISSN: 1532-1924
Titre abrégé: Best Pract Res Clin Haematol
Pays: Netherlands
ID NLM: 101120659
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
30
03
2020
accepted:
27
05
2020
entrez:
11
10
2020
pubmed:
12
10
2020
medline:
8
10
2021
Statut:
ppublish
Résumé
Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of our society, including a burgeoning role in medical research and clinical practice. As the volume of medical data grows at an apparently exponential rate, particularly since the human genome project laid the foundation for modern genetic inquiry, informatics tools like machine learning are becoming crucial in analyzing these data to provide meaningful tools for diagnostic, prognostic, and therapeutic purposes. Within medicine, hematologic diseases can be particularly challenging to understand and treat given the increasingly complex and intercalated genetic, epigenetic, immunologic, and regulatory pathways that must be understood to optimize patient outcomes. In acute myeloid leukemia (AML), new developments in machine learning algorithms have enabled a deeper understanding of disease biology and the development of better prognostic and predictive tools. Ongoing work in the field brings these developments incrementally closer to clinical implementation.
Identifiants
pubmed: 33038981
pii: S1521-6926(20)30053-0
doi: 10.1016/j.beha.2020.101192
pmc: PMC7548395
mid: NIHMS1625527
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
101192Subventions
Organisme : NCI NIH HHS
ID : K12 CA076917
Pays : United States
Informations de copyright
Copyright © 2020. Published by Elsevier Ltd.
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