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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
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 us...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372951/ https://www.ncbi.nlm.nih.gov/pubmed/35962355 http://dx.doi.org/10.1186/s12911-022-01965-9 |
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author | Nishimwe, Aurore Ruranga, Charles Musanabaganwa, Clarisse Mugeni, Regine Semakula, Muhammed Nzabanita, Joseph Kabano, Ignace Uwimana, Annie Utumatwishima, Jean N. Kabakambira, Jean Damascene Uwineza, Annette Halvorsen, Lars Descamps, Freija Houghtaling, Jared Burke, Benjamin Bahati, Odile Bizimana, Clement Jansen, Stefan Twizere, Celestin Nkurikiyeyezu, Kizito Birungi, Francine Nsanzimana, Sabin Twagirumukiza, Marc |
author_facet | Nishimwe, Aurore Ruranga, Charles Musanabaganwa, Clarisse Mugeni, Regine Semakula, Muhammed Nzabanita, Joseph Kabano, Ignace Uwimana, Annie Utumatwishima, Jean N. Kabakambira, Jean Damascene Uwineza, Annette Halvorsen, Lars Descamps, Freija Houghtaling, Jared Burke, Benjamin Bahati, Odile Bizimana, Clement Jansen, Stefan Twizere, Celestin Nkurikiyeyezu, Kizito Birungi, Francine Nsanzimana, Sabin Twagirumukiza, Marc |
author_sort | Nishimwe, Aurore |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9372951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93729512022-08-12 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 Nishimwe, Aurore Ruranga, Charles Musanabaganwa, Clarisse Mugeni, Regine Semakula, Muhammed Nzabanita, Joseph Kabano, Ignace Uwimana, Annie Utumatwishima, Jean N. Kabakambira, Jean Damascene Uwineza, Annette Halvorsen, Lars Descamps, Freija Houghtaling, Jared Burke, Benjamin Bahati, Odile Bizimana, Clement Jansen, Stefan Twizere, Celestin Nkurikiyeyezu, Kizito Birungi, Francine Nsanzimana, Sabin Twagirumukiza, Marc BMC Med Inform Decis Mak Research 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. BioMed Central 2022-08-12 /pmc/articles/PMC9372951/ /pubmed/35962355 http://dx.doi.org/10.1186/s12911-022-01965-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Nishimwe, Aurore Ruranga, Charles Musanabaganwa, Clarisse Mugeni, Regine Semakula, Muhammed Nzabanita, Joseph Kabano, Ignace Uwimana, Annie Utumatwishima, Jean N. Kabakambira, Jean Damascene Uwineza, Annette Halvorsen, Lars Descamps, Freija Houghtaling, Jared Burke, Benjamin Bahati, Odile Bizimana, Clement Jansen, Stefan Twizere, Celestin Nkurikiyeyezu, Kizito Birungi, Francine Nsanzimana, Sabin Twagirumukiza, Marc 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9372951/ https://www.ncbi.nlm.nih.gov/pubmed/35962355 http://dx.doi.org/10.1186/s12911-022-01965-9 |
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