< Back to previous page


Machine learning platform for better cardiovascular health assessment and risk stratification

Precise description of cardiovascular health and risk assessment are essential for optimal prevention and treatment strategies, but require a complex integration of many factors. The specific objectives of this project are: (1) Applying machine-learning (ML) algorithms to the available longitudinal, highly standardized, general population data to assess the complexity of clinical and behavioral 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) The pre-trained ML models are prepared for integration into an open service platform in collaboration with industrial IT partners in healthcare. Special attention will be paid to the implementation of an intuitive graphical user interface and a standardized set of input-predicting variables that enhance the usability of the platform for the medical community.

Date:9 Sep 2019 →  Today
Keywords:Precision Medicine, Cardiovascular Health, Machine Leaning, Risk Stratification, Diastolic dysfunction
Disciplines:Computational biomodelling and machine learning
Project type:PhD project