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Collection, processing and analysis of heterogeneous data coming from Spanish hospitals in the context of COVID-19
dc.contributor.author | Barroso Isidoro, Marta |
dc.contributor.author | Tormos, Adrián |
dc.contributor.author | Pérez Arnal, Raquel Leandra |
dc.contributor.author | Álvarez Napagao, Sergio |
dc.contributor.author | García Gasulla, Dario |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2021-12-16T09:19:27Z |
dc.date.available | 2021-12-16T09:19:27Z |
dc.date.issued | 2021 |
dc.identifier.citation | Barroso, M. [et al.]. Collection, processing and analysis of heterogeneous data coming from Spanish hospitals in the context of COVID-19. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development: proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence". IOS Press, 2021, p. 254-263. ISBN 978-1-64368-211-2. DOI 10.3233/FAIA210142. |
dc.identifier.isbn | 978-1-64368-211-2 |
dc.identifier.uri | http://hdl.handle.net/2117/358644 |
dc.description.abstract | The COVID-19 pandemic has already caused more than 150,000,000 cases worldwide. In Spain this has lead to a massive and simultaneous saturation of all sanitary regions. Coherently, the quick and consistent understanding of the COVID-19 disease requires of the combined analysis of thousands of medical records generated by dozens of different institutions. In the context of the publicly funded CIBERES-UCI-COVID project, we have gathered, cleaned and preprocessed data from heterogeneous sources – more than 30 hospitals, with different data entry systems – in order to produce a unified database, of more than 6.000 patients, that is used in several clinical studies being carried by different multidisciplinary groups. In this paper, we identify the complexities we encountered, the solutions we applied, and we summarise the statistical and machine learning techniques we have applied for the studies. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | IOS Press |
dc.rights | Attribution-NonCommercial 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació::Bases de dades |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Databases |
dc.subject.lcsh | COVID-19 (Disease) |
dc.subject.other | Data migration |
dc.subject.other | Continuous development and integration (CD/CI) |
dc.subject.other | Automated report generation |
dc.title | Collection, processing and analysis of heterogeneous data coming from Spanish hospitals in the context of COVID-19 |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Bases de dades |
dc.subject.lemac | COVID-19 (Malaltia) |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.identifier.doi | 10.3233/FAIA210142 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://ebooks.iospress.nl/doi/10.3233/FAIA210142 |
dc.rights.access | Open Access |
local.identifier.drac | 32296968 |
dc.description.version | Postprint (published version) |
local.citation.author | Barroso, M.; Tormos, A.; Pérez, R.; Álvarez-Napagao, S.; García, D. |
local.citation.contributor | International Conference of the Catalan Association for Artificial Intelligence |
local.citation.publicationName | Artificial Intelligence Research and Development: proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence |
local.citation.startingPage | 254 |
local.citation.endingPage | 263 |