Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.
Commun Med (Lond)
; 3(1): 68, 2023 May 17.
Article
em En
| MEDLINE
| ID: mdl-37198246
There are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model a type of computer model that learns from patterns in data without human inputto separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Commun Med (Lond)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha