Publication
An interactive dashboard for statistical analysis of intensive care unit COVID-19 data
| dc.contributor.author | Dias, Rúben | |
| dc.contributor.author | Ferreira, Artur | |
| dc.contributor.author | Pinto, Iola | |
| dc.contributor.author | Brás-Geraldes, Carlos | |
| dc.contributor.author | Von Rekowski, Cristiana | |
| dc.contributor.author | Bento, Luís | |
| dc.date.accessioned | 2024-03-27T17:18:16Z | |
| dc.date.available | 2024-03-27T17:18:16Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | COVID-19 caused a pandemic, due to its ease of transmission and high number of infections. The evolution of the pandemic and its consequences for the mortality and morbidity of populations, especially the elderly, generated several scientific studies and many research projects. Among them, we have the Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine (PREMO) research project. For such a project with many data records, it is necessary to provide a smooth graphical analysis to extract value from it. Methods: In this paper, we present the development of a full-stack Web application for the PREMO project, consisting of a dashboard providing statistical analysis, data visualization, data import, and data export. The main aspects of the application are described, as well as the diverse types of graphical representations and the possibility to use filters to extract relevant information for clinical practice. Results: The application, accessible through a browser, provides an interactive visualization of data from patients admitted to the intensive care unit (ICU), throughout the six waves of COVID-19 in two hospitals in Lisbon, Portugal. The analysis can be isolated per wave or can be seen in an aggregated view, allowing clinicians to create many views of the data and to study the behavior and consequences of different waves. For instance, the experimental results show clearly the effect of vaccination as well as the changes on the most relevant clinical parameters on each wave. Conclusions: The dashboard allows clinicians to analyze many variables of each of the six waves as well as aggregated data for all the waves. The application allows the user to extract information and scientific knowledge about COVID-19’s evolution, yielding insights for this pandemic and for future pandemics. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Dias R, Ferreira A, Pinto I, Geraldes C, Von Rekowski C, Bento L. An Interactive Dashboard for Statistical Analysis of Intensive Care Unit COVID-19 Data. BioMedInformatics. 2024; 4(1):454-476. https://doi.org/10.3390/biomedinformatics4010026 | pt_PT |
| dc.identifier.doi | 10.3390/biomedinformatics4010026 | pt_PT |
| dc.identifier.eissn | 2673-7426 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/17229 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/2673-7426/4/1/26 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | COVID-19 | pt_PT |
| dc.subject | dashboard | pt_PT |
| dc.subject | data visualization | pt_PT |
| dc.subject | intensive care unit | pt_PT |
| dc.subject | Kaplan–Meier survival curves | pt_PT |
| dc.subject | Lisbon hospitals | pt_PT |
| dc.subject | pandemic waves | pt_PT |
| dc.subject | PREMO project | pt_PT |
| dc.subject | statistical analysis | pt_PT |
| dc.subject | Web application | pt_PT |
| dc.title | An interactive dashboard for statistical analysis of intensive care unit COVID-19 data | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0117%2F2020/PT | |
| oaire.citation.endPage | 23 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | BiomedInformatics | pt_PT |
| oaire.fundingStream | 3599-PPCDT | |
| person.familyName | Ferreira | |
| person.familyName | Pinto | |
| person.familyName | Brás-Geraldes | |
| person.familyName | Von Rekowski | |
| person.givenName | Artur | |
| person.givenName | Iola | |
| person.givenName | Carlos | |
| person.givenName | Cristiana | |
| person.identifier | R-000-ME5 | |
| person.identifier.ciencia-id | 5715-2B2D-9ADC | |
| person.identifier.ciencia-id | 081A-ABD6-13DD | |
| person.identifier.orcid | 0009-0002-9141-3638 | |
| person.identifier.orcid | 0000-0002-2945-1441 | |
| person.identifier.orcid | 0000-0002-1551-6531 | |
| person.identifier.orcid | 0009-0009-6843-1935 | |
| person.identifier.rid | A-4358-2019 | |
| person.identifier.scopus-author-id | 34969436900 | |
| person.identifier.scopus-author-id | 57197972944 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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