Forecasting mental states in schizophrenia using digital phenotyping data.
The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. Previous research focused on binary classification and continuous regression, disregarding the often ordinal nature of predic...
Saved in:
Main Authors: | Thierry Jean, Rose Guay Hottin, Pierre Orban |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2025-02-01
|
Series: | PLOS Digital Health |
Online Access: | https://doi.org/10.1371/journal.pdig.0000734 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Assessment of Negative Symptoms in Schizophrenia: From the Consensus Conference-Derived Scales to Remote Digital Phenotyping
by: Armida Mucci, et al.
Published: (2025-01-01) -
Beyond schizophrenia: living and working with a serious mental illness
by: Peter G. Bota, et al.
Published: (2017-03-01) -
Digital sleep phenotype and wrist actigraphy in individuals at clinical high risk for psychosis and people with schizophrenia spectrum disorders: a systematic review and meta-analysis
by: Andrea Cipriani, et al.
Published: (2025-02-01) -
Attentional Phenotypes for the Analysis of Higher Mental Function
by: John Fossella, et al.
Published: (2002-01-01) -
Mental health phenotypes of well-controlled HIV in Uganda
by: Leah H. Rubin, et al.
Published: (2025-01-01)