The CQC require an automatic tool to analyse large numbers of online patient comments and detect significant negative changes in care quality. This poses challenges which distinguish it from standard text mining problems, including: 1. A highly unbalanced dataset: cases of interest will make up only a very small percentage of the data (c.0.5%); 2. A variable domain: the tool must cope with comments from a range of online sources, and be adaptable to social media in future; 3. A sensitive use case: the desired characteristics of the output (in particular, whether it is more important to av…
- Procurement
- Text Severity
- Published
- 28 Mar 2018
- Status
- complete
- CPV
- 79000000 — Business services: law, marketing, consulting, recruitment, printing and security
- Tender period
- — – 25 Jan 2018
Awards
Other notices in this procurement
FAQs about this notice
What type of notice is this?
This is a award update published via Contracts Finder on 28 Mar 2018.
Which procurement does this notice belong to?
It is part of "Text Severity", published by Care Quality Commission.
Has this notice resulted in an award?
1 award is recorded on this notice, including to QUEEN MARY UNIVERSITY OF LONDON.
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