Software Sprint - Single project: OpenResearch - Open Research on COVID19
- Funded by Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)
- Total publications:0 publications
Grant number: 01IS20S28
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Key facts
Disease
COVID-19Start & end year
20202020Known Financial Commitments (USD)
$52,119.1Funder
Bundesministerium für Bildung und Forschung [German Federal Ministry of Education and Research] (BMBF)Principal Investigator
N/A
Research Location
GermanyLead Research Institution
OpenResearchCOVID19 - Leonardo de Araújo, Nina Hentschel & Hanna Blonska GbRResearch Priority Alignment
N/A
Research Category
Health Systems Research
Research Subcategory
Health information systems
Special Interest Tags
Data Management and Data Sharing
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
The plan is to develop a modular, extensible & open API platform that will provide AI services to extract insights & information from the large number of scientific writings on COVID-19. During the grant period, 3 AI services based on Topic Modeling, Doc2Vec & Q&A with SQuAD+Bert will be developed. The combination of these provides a summary & enables semantically rich queries on the scientific content. The API platform integrates with dashboards, websites & 3rd party platforms. The goal is not only to offer insight into the data, but also to allow users to train their own data models. Although COVID-19 is a new disease, already more than 52,000 wiss. Publications have been written on this & more are being added daily. Doctors, physicians & researchers do not have the time to read all the scientific publications, especially if they are in front of a scientific meeting. Publications, especially when they are faced with solving a specific problem, which is not the main topic of the essays, but the solution is contained in them. Helping healthcare professionals cope with the complexity of large volumes of scientific material & enabling them to perform user-friendly data retrieval using NLP techniques are critical attributes for increasing response effectiveness to current & future challenges.