(COVID-19 PROMPT) Toward PrecisiOn Medicine for the Prediction of Treatment response to Covid-19 in Cuba

Project Details

Description

The project aims at improving the quality of the Cuban health system services and population well-being (SGD 3), thru the development of models and tools for the application of personalized medicine. The project aims at the analysis of disease–patient dynamics that updates according to changes in COVID19 patient’s condition, while in hospital, to facilitate patient management and resource allocation. It focuses on (i) the implementation of intelligent software methods for the quantification of pulmonary conditions in chest imaging, and (ii) the implementation of AI-based models for the prediction of COVID-19 disease trajectory combining patient’s health records and chest imaging. The goal is to provide doctors in Cuban public hospitals with a tool that helps them make decisions and allows them to better guide treatments and effectively take advantage of existing resources and technologies to save the lives of a greater number of patients.
Short title or EU acronymCOVID-19 PROMPT
AcronymVLIR409
StatusFinished
Effective start/end date1/09/2231/08/24

Keywords

  • COVID-19
  • Medicine
  • Cuba

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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
  • Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia

    Garea-Llano, E., Diaz-Berenguer, A., Sahli, H. & Gonzalez-Dalmau, E., 9 Jun 2023, Lecture Notes in Computer Science: Mexican Conference on Pattern Recognition. Rodríguez-González, A. Y., Pérez-Espinosa, H., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A. & Olvera-López, J. A. (eds.). Springer, Cham, Vol. 13902. p. 211–220 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13902 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

  • Representation Learning with Information Theory to Detect COVID-19 and its Severity

    Diaz Berenguer, A., Mukherjee, T., Da, Y., Bossa Bossa, M. N., Kvasnytsia, M., Vandemeulebroucke, J., Deligiannis, N. & Sahli, H., 23 Feb 2023, Lecture Notes in Computer Science: Computer Vision – ECCV 2022 Workshops. Karlinsky, L., Michaeli, T. & Nishino, K. (eds.). Springer, Cham, Vol. 13807. p. 605-620 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 13807 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

    1 Citation (Scopus)