Dementia detection using automatic analysis of conversations
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2508
- Type
- D - Journal article
- DOI
-
10.1016/j.csl.2018.07.006
- Title of journal
- Computer Speech and Language
- Article number
- -
- First page
- 65
- Volume
- 53
- Issue
- -
- ISSN
- 0885-2308
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
G - Speech and Hearing
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first paper to demonstrate fully automatic dementia diagnosis based on interaction analysis between the patient and virtual doctor, extensively and successfully tested in real clinical settings with 200+ patients. It led to £315k funding from Rosetrees Trust Interdisciplinary Prize (2019) on Medicine & AI; NIHR; MRC and underpinned an EPSRC 6-month postdoctoral fellowship, addressing the use of conversational agents for pathology detection, mental health self-management and stroke-survivor cognitive health monitoring. Furthermore, it orchestrated two knowledge exchange grants (HEIF Research England) with Rosetrees and SME TherapyBox, and two PhD projects sponsored by Apple and Epilepsy Research UK.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -