Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

Abstract Background Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cann...

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Bibliographic Details
Main Authors: Aurore Nishimwe, Charles Ruranga, Clarisse Musanabaganwa, Regine Mugeni, Muhammed Semakula, Joseph Nzabanita, Ignace Kabano, Annie Uwimana, Jean N. Utumatwishima, Jean Damascene Kabakambira, Annette Uwineza, Lars Halvorsen, Freija Descamps, Jared Houghtaling, Benjamin Burke, Odile Bahati, Clement Bizimana, Stefan Jansen, Celestin Twizere, Kizito Nkurikiyeyezu, Francine Birungi, Sabin Nsanzimana, Marc Twagirumukiza
Format: Article
Language:English
Published: BMC 2022-08-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Artificial intelligence
Machine learning
Data science
SARS-COV-2/COVID-19
Rwanda
Online Access:https://doi.org/10.1186/s12911-022-01965-9

Internet

https://doi.org/10.1186/s12911-022-01965-9

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