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Collection, processing and analysis of heterogeneous data coming from Spanish hospitals in the context of COVID-19
- Source :
- UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), CCIA
- Publication Year :
- 2021
- Publisher :
- IOS Press, 2021.
-
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. © 2021 The authors and IOS Press.
- Subjects :
- 2019-20 coronavirus outbreak
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC]
Coronavirus disease 2019 (COVID-19)
Computer science
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Informàtica::Sistemes d'informació::Bases de dades [Àrees temàtiques de la UPC]
Context (language use)
Data entry
Data science
Data migration
COVID-19 (Malaltia)
Bases de dades
Databases
Continuous development and integration (CD/CI)
COVID-19 (Disease)
Multidisciplinary approach
Machine learning
Aprenentatge automàtic
Automated report generation
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), CCIA
- Accession number :
- edsair.doi.dedup.....6ed5fc0b40d189c9507c8632671478f2
- Full Text :
- https://doi.org/10.3233/FAIA210142