Inter-battery Topic Representation Learning
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 8947
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-46484-8_13
- Title of conference / published proceedings
- 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII
- First page
- 210
- Volume
- 9912
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2016
- 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
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A large number of patients seek medical assistance due to non-specific pain. In order to assign the correct medical treatment patients are asked to draw their discomfort on a paper. However, diagnosis from such drawings is challenging as individual patients will use different visual cues to characterise their pain. The work developed in this paper lead to an application of automatic diagnosis from discomfort drawings where the unique individual traits of a person could be factorised from the indicative parts of an illness thereby leading to both robust and explainable diagnosis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -