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.

Autor: Nishimwe A; College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda. auroreshimwa@yahoo.fr., Ruranga C; African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda., Musanabaganwa C; Rwanda Biomedical Center, Ministry of Health, Kigali, Rwanda., Mugeni R; Rwamagana Provincial Hospital, East province, Rwamagana, Rwanda., Semakula M; Rwanda Biomedical Center, Ministry of Health, Kigali, Rwanda., Nzabanita J; College of Science and Technology, University of Rwanda, Kigali, Rwanda., Kabano I; African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda., Uwimana A; African Center of Excellence in Data Science, University of Rwanda, Kigali, Rwanda., Utumatwishima JN; Rwamagana Provincial Hospital, East province, Rwamagana, Rwanda., Kabakambira JD; The University Teaching Hospital of Kigali (CHUK), Kigali, Rwanda., Uwineza A; The University Teaching Hospital of Kigali (CHUK), Kigali, Rwanda., Halvorsen L; edenceHealth NV, Kontich, Belgium., Descamps F; edenceHealth NV, Kontich, Belgium., Houghtaling J; edenceHealth NV, Kontich, Belgium., Burke B; edenceHealth NV, Kontich, Belgium., Bahati O; Regional Alliance of Sustainable Development, Kigali, Rwanda., Bizimana C; Regional Alliance of Sustainable Development, Kigali, Rwanda., Jansen S; College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda., Twizere C; Center of Excellence in Biomedical Engineering and eHealth, University of Rwanda, Kigali, Rwanda., Nkurikiyeyezu K; Center of Excellence in Biomedical Engineering and eHealth, University of Rwanda, Kigali, Rwanda., Birungi F; College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda., Nsanzimana S; Rwanda Biomedical Center, Ministry of Health, Kigali, Rwanda., Twagirumukiza M; College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda.; Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
Jazyk: angličtina
Zdroj: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2022 Aug 12; Vol. 22 (1), pp. 214. Date of Electronic Publication: 2022 Aug 12.
DOI: 10.1186/s12911-022-01965-9
Abstrakt: 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 cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.
Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.
Expected Results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini ("data node"), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.
Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.
(© 2022. The Author(s).)
Databáze: MEDLINE
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