Cargando…
Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment
In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cogniti...
Autores principales: | Santana, Alex Novaes, de Santana, Charles Novaes, Montoya, Pedro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697204/ https://www.ncbi.nlm.nih.gov/pubmed/33212774 http://dx.doi.org/10.3390/diagnostics10110958 |
Ejemplares similares
-
Using Deep Learning and Resting-State fMRI to Classify Chronic Pain Conditions
por: Santana, Alex Novaes, et al.
Publicado: (2019) -
The added value of bedside examination and screening QST to improve neuropathic pain identification in patients with chronic pain
por: Timmerman, Hans, et al.
Publicado: (2018) -
QST Conference CICG 16 Oct 2017
por: Ordan, Julien Marius
Publicado: (2017) -
Outcomes of a QST Protocol in Healthy Subjects and Chronic Pain Patients: A Controlled Clinical Trial
por: Dias, Patrícia, et al.
Publicado: (2023) -
Peer review of the pesticide risk assessment of the active substance Bacillus amyloliquefaciens strain QST 713 (formerly Bacillus subtilis strain QST 713)
por: Anastassiadou, Maria, et al.
Publicado: (2021)