Taking a closer look at survival modeling with ECGs
The Cava laboratory is a research group interested in improving the interpretability and fairness of predictive models deployed in healthcare settings. We create and study algorithms that can embed these notions when working with health data.
The lab is part of the Computational Health Informatics Program at Boston Children’s Hospital, affiliated with Harvard Medical School.
Join the Team! If you’re looking to do research in the areas of machine learning, interpretability, fairness, and health informatics, see the opportunities here.
Recent Papers
Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study
The Lancet Digital Health
A review of “Symbolic Regression” by Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan M. Winkler, and Michael Affenzeller, ISBN 978-1-138-05481-3, 2024, CRC Press.
Genetic Programming and Evolvable Machines
Recent Posts
Relaxing the definition of equivalent mathematical expressions to get simpler and more interpretable models
About our recent HUMIES award-winning algorithm for clinical prediction models