What is it about?
Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records (EHRs). We aimed to explore ML-CPMs applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.
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Why is it important?
We are trying to apply new methods referring to artificial intelligence to predict mortality in medical structures beyond the classical statistical indicators currently applied.
Perspectives
The new methods aim to better predict and manage medical resources, improve the quality of life and thus improve the quality of health services.
PhD candidate Dimitrios Dimopoulos
University of the Aegean
Read the Original
This page is a summary of: Machine learning-based predictive models for patients with venous thromboembolism: A Systematic Review, Thrombosis and Haemostasis, April 2024, Thieme Publishing Group,
DOI: 10.1055/a-2299-4758.
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