Our project addresses the challenges of conveying data curation results in the clinical and healthcare domain by fulfilling the following concrete research objectives (numbered from 1 to 4). (O1) Exploratory analysis and collection of anonymized datasets. (O2) Declarative specification of quality indicators and annotations. (O3) Quality-aware query answering and refinement. (O4) Quality Indicators-driven Analytics. The novelty of the QualiHealth project resides in the design of a full-fledged Quality Indicators(QI)- driven analytical platform allowing to combine specification tasks for quality indicators tailored for the clinical and preclinical data with query answering and human-guided query refinement tasks, along with complex analytical and learning tasks. It seamlessly addresses the needs of computer scientists, data scientists and medical doctors in tandem, by providing a unified framework where all these actors can rely on automated and semi-automated techniques to build quality-aware analytical tasks. As tangible results of our project, we expect a quality-certified collection of medical and biological datasets, on which data quality-certified analytical queries can be formulated. We also envision the design and implementation of a quality-aware query engine. Our objective is also to contribute to the advances on data curation, data cleaning, and highly complex analytics for healthcare data, which to the best of our knowledge is not existing at present in France.
- LIRIS UMR 5205 - LABORATOIRE D'INFORMATIQUE EN IMAGE ET SYSTEMES D'INFORMATION
- LIMOS Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
- CRC CENTRE DE RECHERCHE DES CORDELIERS
- Institut Cochin IMAG'IC
- GNUBILA MAAT FRANCE
- LIS Laboratoire d'Informatique et Systèmes
- UBC University of British Columbia / Department of Computer Science