Machine learning of genomic factors in 1,961 patients with acute myeloid leukemia identifies patients with very good or very poor prognosis who do not benefit from allogeneic hematopoietic cell transplant in first remission Meeting Abstract
| Authors: | Fleming, S.; Tsai, C. H.; Dohner, H.; Dohner, K.; Papaemmanuil, E.; Tien, H. F.; Reynolds, J.; Wei, A. H.; Hou, H. A. |
| Abstract Title: | Machine learning of genomic factors in 1,961 patients with acute myeloid leukemia identifies patients with very good or very poor prognosis who do not benefit from allogeneic hematopoietic cell transplant in first remission |
| Meeting Title: | 63nd Annual Meeting of the American Society of Hematology (ASH) |
| Journal Title: | Blood |
| Volume: | 138 |
| Issue: | Suppl. 1 |
| Meeting Dates: | 2021 Dec 11-14 |
| Meeting Location: | Atlanta, GA |
| ISSN: | 0006-4971 |
| Publisher: | American Society of Hematology |
| Date Published: | 2021-11-23 |
| Language: | English |
| ACCESSION: | WOS:000736398801002 |
| DOI: | 10.1182/blood-2021-151972 |
| PROVIDER: | wos |
| Notes: | Meeting Abstract: 225 -- Hybrid meeting, also took place online -- Source: Wos |
