A Novel Particle Swarm Optimization Approach for Patient Clustering from Emergency Departments
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 064-193000-10340
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2018.2878536
- Title of journal
- Ieee Transactions On Evolutionary Computation
- Article number
- -
- First page
- 632
- Volume
- 23
- Issue
- 4
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
http://bura.brunel.ac.uk/handle/2438/17403
- 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)
-
1 - Artificial Intelligence (AI)
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research paper originates from NHS funded research (BRAD: B61BB1B0-9279-4AE4-B172-F9220C05895D) from the Hillingdon Clinical Commissioning Group (CCG) examining urgent care patient admittance and discharge in West London. The algorithm reported here was used to determine local patient groups within the whole system simulation of potential urgent care strategies (from ambulance to discharge) and supported key strategy choices by the Chief Operating Office Ceri Jacobs and Mark Eaton (contact the CCG Chair Ian Goodman for confirmation ian.goodman@nhs.net). The paper was published in a journal ranked 3rd out of 137 in Computer Science: Artificial Intelligence (2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
