Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions.
acoustic
conversation
digital phenotype
facial feature
voice
Journal
Computational psychiatry (Cambridge, Mass.)
ISSN: 2379-6227
Titre abrégé: Comput Psychiatr
Pays: England
ID NLM: 101719151
Informations de publication
Date de publication:
2022
2022
Historique:
received:
02
08
2021
accepted:
13
12
2021
medline:
11
1
2022
pubmed:
11
1
2022
entrez:
22
5
2024
Statut:
epublish
Résumé
We conducted a feasibility analysis to determine the quality of data that could be collected ambiently during routine clinical conversations. We used inexpensive, consumer-grade hardware to record unstructured dialogue and open-source software tools to quantify and model face, voice (acoustic and language) and movement features. We used an external validation set to perform proof-of-concept predictive analyses and show that clinically relevant measures can be produced without a restrictive protocol.
Identifiants
pubmed: 38774775
doi: 10.5334/cpsy.78
pmc: PMC11104416
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1-7Informations de copyright
Copyright: © 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors have no competing interests to declare.