Deriving symptom networks from digital phenotyping data in serious mental illness.

ABSTRACT:

Background

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

Aims

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

Method

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into

SUBMITTER: Hays R 

PROVIDER: S-EPMC7745255 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature