Editorial. Special section on artificial intelligence for diabetes
This special section of this issue of the Artificial Intelligence in Medicine (AIIM) journal originates from the First Workshop on Artificial intelligence for Diabetes (AID 2016) on 30th August 2016. The workshop was part of the 22nd European Conference on Artificial Intelligence (ECAI 2016) in The...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/17871 |
| Acesso em linha: | http://hdl.handle.net/10256/17871 |
| Access Level: | acceso abierto |
| Palavra-chave: | Intel·ligència artificial -- Aplicacions a la medicina Artificial intelligence -- Medical applications Diabetis Diabetes |
| Resumo: | This special section of this issue of the Artificial Intelligence in Medicine (AIIM) journal originates from the First Workshop on Artificial intelligence for Diabetes (AID 2016) on 30th August 2016. The workshop was part of the 22nd European Conference on Artificial Intelligence (ECAI 2016) in The Hague, Holland. Authors with papers accepted for the workshop were subsequently invited to revise and extend their work for publication in this issue, and a wider call was also announced to attract work from other outstanding researchers in the area. Authors were invited to submit original contributions on the overarching theme of Artificial Intelligence-based solutions to problems associated with diabetes. In particular, papers were sought on topics including Intelligent solutions to empower citizens with self-management of health conditions; Intelligent systems for glucose prediction and alarm generation; clinical decision support tools to deal with the avalanche of data gathered by sensors; data mining approaches for risk prediction and prevention of diabetes comorbidities; as well as community tools, platforms to support research in this area and data sets for benchmarkin |
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