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.
If you’re looking to do research in the areas of machine learning, interpretability, fairness, and health informatics, see the opportunities here.
Recent Papers
Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models
Machine Learning for Healthcare
Call for Action: towards the next generation of symbolic regression benchmark
Proceedings of the Genetic and Evolutionary Computation Conference Companion
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