Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator
| dc.contributor.author | Dunn, Anthony J. | |
| dc.contributor.author | Coniglio, Stefano | |
| dc.contributor.author | ElRefai, Mohamed | |
| dc.contributor.author | Roberts, Paul R. | |
| dc.contributor.author | Wiles, Benedict M. | |
| dc.contributor.author | Zemkoho, Alain B. | |
| dc.contributor.institution | University of Aberdeen.Medical Sciences | en |
| dc.date.accessioned | 2023-10-31T14:53:01Z | |
| dc.date.available | 2023-10-31T14:53:01Z | |
| dc.date.issued | 2023-09 | |
| dc.description | Open access funding provided by Università degli studi di Bergamo within the CRUI-CARE Agreement. The work of Anthony J. Dunn is jointly funded by Decision Analysis Services Ltd andf EPSRC through the studentship with Reference EP/R513325/1. The work of Alain B. Zemkoho is supported by the EPSRC grant EP/V049038/1. The work of Stefano Coniglio and Alain B. Zemkoho is supported by The Alan Turing Institute under the EPSRC grants EP/N510129/1 and EP/W037211/1. | en |
| dc.description.status | Peer reviewed | en |
| dc.format.extent | 27 | |
| dc.format.extent | 2182939 | |
| dc.identifier | 281642847 | |
| dc.identifier | 36078245-cd89-4833-8992-a6439ce31a3b | |
| dc.identifier | 85160251694 | |
| dc.identifier.citation | Dunn, A J, Coniglio, S, ElRefai, M, Roberts, P R, Wiles, B M & Zemkoho, A B 2023, 'Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator', Annals Of Operations Research, vol. 328, pp. 309-335. https://doi.org/10.1007/s10479-023-05326-1 | en |
| dc.identifier.doi | 10.1007/s10479-023-05326-1 | |
| dc.identifier.issn | 0254-5330 | |
| dc.identifier.other | ORCID: /0000-0002-1226-5335/work/146067848 | |
| dc.identifier.uri | https://hdl.handle.net/2164/22072 | |
| dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85160251694&partnerID=8YFLogxK | en |
| dc.identifier.vol | 328 | en |
| dc.language.iso | eng | |
| dc.relation.ispartof | Annals Of Operations Research | en |
| dc.subject | SDG 3 - Good Health and Well-being | en |
| dc.subject | Deep learning | en |
| dc.subject | Machine learning | en |
| dc.subject | Optimization | en |
| dc.subject | Subcutaneous implantable cardioverter defibrillators | en |
| dc.subject | R Medicine | en |
| dc.subject | General Decision Sciences | en |
| dc.subject | Management Science and Operations Research | en |
| dc.subject | Engineering and Physical Sciences Research Council (EPSRC) | en |
| dc.subject | EP/R513325/1 | en |
| dc.subject | EP/V049038/1 | en |
| dc.subject | EP/N510129/1 | en |
| dc.subject | EP/W037211/1 | en |
| dc.subject.lcc | R | en |
| dc.title | Deep learning and hyperparameter optimization for assessing one’s eligibility for a subcutaneous implantable cardioverter-defibrillator | en |
| dc.type | Journal article | en |
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