Cargando…
Machine-learned identification of psychological subgroups with relation to pain interference in patients after breast cancer treatments
BACKGROUND: Persistent pain in breast cancer survivors is common. Psychological and sleep-related factors modulate perception, interpretation and coping with pain and may contribute to the clinical phenotype. The present analysis pursued the hypothesis that breast cancer survivors form subgroups, ba...
Autores principales: | Sipilä, Reetta, Kalso, Eija, Lötsch, Jörn |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375580/ https://www.ncbi.nlm.nih.gov/pubmed/32066081 http://dx.doi.org/10.1016/j.breast.2020.01.042 |
Ejemplares similares
-
Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
por: Lötsch, Jörn, et al.
Publicado: (2018) -
Pain-related and psychological factors mediate the effect of personality on health-related quality of life. A study in breast cancer survivors with persistent pain
por: Aho, Tommi, et al.
Publicado: (2023) -
Machine-learned analysis of global and glial/opioid intersection–related DNA methylation in patients with persistent pain after breast cancer surgery
por: Kringel, Dario, et al.
Publicado: (2019) -
Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation
por: Lötsch, Jörn, et al.
Publicado: (2022) -
Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes
por: Miettinen, Teemu, et al.
Publicado: (2022)