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Predicting Cardiovascular Events Using Machine Learning


Validation and Improvement of ML models across different hospital networks

WP-1 Data management & quality
WP-2 Model validation & adaptation, different cohorts
WP-3 Federated learning for model improvement


Integration of ML models in HIS and evaluation in daily hospital routine

WP-4 Integration of best performing models in HIS
WP-5 Evaluation of the models in prospective clinical settings


Effective risk communication and fair predictions

WP-6 Evaluation of effective/personalised risk communication strategies
WP-7 Personalised health services
WP-8 Evaluation of biases in machine learning models

Project Aims

Cardiovascular disease is the leading cause of death worldwide. Underlying atherosclerosis and ensuing conditions such as myocardial infarction, ischemic heart disease and stroke cause tremendous morbidity, mortality and economic loss. Early identification of patients at high risk for such clinical events enables preventive actions. The use of machine learning (ML) for risk prediction can outperform traditional risk scores. Although many ML models have been developed over the last years, validation is rare. We do not yet know how models perform in different clinical settings or populations. Furthermore, as models use numerous and diverse predictors, it is hard to transfer models to other health systems.

Recently, we developed risk prediction models for major adverse cardiovascular events and progression of kidney disease. However, the models lack external validation, hindering implementation in different clinical contexts and limiting generalizability.

As such, this project has three main aims. First, we aim to validate and improve our ML models across different hospital networks and populations. Second, we aim to integrate ML models in different hospital information systems and evaluate their impact on daily hospital routine. Third, building upon these validated models, we aim to address effective risk communication strategies in order to effect behavioural changes in patients. Therefore, our project makes a fundamental contribution towards employing innovative personalized risk prediction and to assess its clinical implementation in a transnational context.