Machine learning platform for better cardiovascular health assessment and risk stratification
Description: A precise description of cardiovascular health and risk assessment are essential for optimal prevention and treatment strategies, but require a complex integration of many factors. We thus propose development of a machine learning platform with direct application in clinical practice. The specific objectives of this project are: (1) Applying machine-learning (ML) algorithms to the available longitudinal, standardized, general population data to assess the complexity of clinical variables, cardiac imaging sequences and pathophysiologically relevant circulating proteins and metabolites for construction of precise cardiovascular classifiers. These computer models extract maximum information from individual clinical data, the most sensitive biomarkers and diagnostic imaging phenotypes for better cardiovascular risk prediction. (2) Refined and validated ML pipelines will be integrated into a dedicated technological platform. It will be used for external validation and further improvement of the models. Final models will be used to produce a software package for clinicians. Special attention will be paid to data protection, intuitive graphical user interface and a standardized set of input-predicting variables that enhance the usability of the platform for the medical community.
Funder: Fonds Wetenschappelijk Onderzoek
Funding type: grant
Funder scheme: FWO Strategic Basic Research Grant
Start date: 2022-11-01
End date: 2024-10-31
Investigator: Frantisek Sabovcik
Institution: FWO
Funded outputs count: 0
Funder award ID: 1S07423N
OpenAlex ID: G7790613776