Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.

Mari, Tyler; Henderson, Jessica; Maden, Michelle; Nevitt, Sarah; Duarte, Rui; Fallon, Nicholas

Mari, Tyler; Henderson, Jessica; Maden, Michelle; Nevitt, Sarah; Duarte, Rui; Fallon, Nicholas.

Afiliación

Mari T; Department of Psychology, University of Liverpool, Liverpool, UK. Electronic address: Tyler.Mari@liverpool.ac.uk.
Henderson J; Department of Psychology, University of Liverpool, Liverpool, UK.
Maden M; Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK.
Nevitt S; Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK.
Duarte R; Department of Health Data Science, Liverpool Reviews and Implementation Group, University of Liverpool, Liverpool, UK.
Fallon N; Department of Psychology, University of Liverpool, Liverpool, UK.

J Pain ; 23(3): 349-369, 2022 Mar.

Article en En | MEDLINE | ID: mdl-34425248

Asunto(s)

Algoritmos; Aprendizaje Automático; Electroencefalografía; Humanos; Dolor/diagnóstico; Dimensión del Dolor; Fenotipo; Resultado del Tratamiento

Palabras clave

Machine learning; pain intensity; pain phenotypes; systematic review; treatment response

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Pain Asunto de la revista: NEUROLOGIA / PSICOFISIOLOGIA Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos

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