Cargando…
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
Mobile technologies offer new opportunities for prospective, high resolution monitoring of long-term health conditions. The opportunities seem of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, deriving clinically meaningful...
Autores principales: | Perez Arribas, Imanol, Goodwin, Guy M., Geddes, John R., Lyons, Terry, Saunders, Kate E. A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293318/ https://www.ncbi.nlm.nih.gov/pubmed/30546013 http://dx.doi.org/10.1038/s41398-018-0334-0 |
Ejemplares similares
-
Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder
por: Carr, Oliver, et al.
Publicado: (2018) -
Circadian rest-activity patterns in bipolar disorder and borderline personality disorder
por: McGowan, Niall M., et al.
Publicado: (2019) -
Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations
por: Gillett, George, et al.
Publicado: (2021) -
Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder
por: Carr, O., et al.
Publicado: (2018) -
Heart rate variability in bipolar disorder and borderline personality disorder: a clinical review
por: Carr, Oliver, et al.
Publicado: (2018)