RES-Q+ is a project funded by the European Union’s Horizon Europe Framework Programme for Research and Innovation, call “Tools and technologies for a healthy society (2021)” (HORIZON-HLTH-2021-TOOL-06).
RES-Q+ builds upon the successful RES-Q (REgistry of Stroke Care Quality) project, currently used by 80+ EU and other countries around the world for collecting and analysing quality of stroke care. RES-Q+ will expand this work by capturing not only in-hospital information but the entire patient pathway including post discharge care.
Project website: https://www.resqplus.eu/
CORDIS website: https://cordis.europa.eu/project/id/101057603
Contact: Svetla Boytcheva
RES-Q+ will assist healthcare professionals by providing a solution for automated ingestion of hospital discharge reports in different languages into a stroke global register. This is achieved by combining NLP with a clinically-validated semantic model towards creating a standard model for such reports and using Artificial Intelligence (AI) to impute missing data.
In addition, the RES-Q+ consortium will develop two novel AI voice assistants, one to help patients provide feedback on their health and the other to help physicians provide high quality care.
This will be the basis for a European Open Stroke Data Platform, an open research platform for data aggregation, semantic harmonization and interoperability across European countries to promote the use and re-use of health data. This will facilitate efforts to define a standard European Stroke Hospital Discharge Report Exchange Format as a tool for better secondary use of data and healthcare in general.
Within the RES-Q+ project, Sirma AI (trading as Ontotext) leads the work on Semantic Interoperability and Data Re-use. This includes harmonizing data from various inter/national registries and other information systems; defining a semantic data model aligned with key medical ontologies; establishing data FAIRification workflows; and more.
In addition, Ontotext will greatly aid the effort in Text Mining of Unstructured Healthcare Data. Towards this end, we will develop tools for automatic information extraction from Healthcare documents as well as for machine translation of such documents into English.
Finally, Ontotext will contribute to establishing an European Open Stroke Data Platform by integrating within it the data retrieved by the unstructured data process tool as well as the semantic data harmonization layer.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No:101057603. Views and opinions expressed are however those of the author only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.