Research in Computational Precision Health brings the computational insights from data enabled by the disciplines of computer science and statistics to bear on applied problems in individual and population health.
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography
Dr. Ahmed Alaa et al develop a unified evaluation protocol, the echocardiographic task adaptation benchmark (ETAB). Echocardiography is one of the most commonly used non-invasive imaging techniques in Cardiology. ETAB is a benchmarking framework for developing deep learning models of echocardiograms using publicly available data sets.
Ethical Machine Learning in Healthcare
Dr. Irene Chen, et al frame ethics of ML in healthcare through the lens of social justice. They describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post deployment considerations.
Optimizing risk-based breast cancer screening policies with reinforcement learning
Dr. Adam Yala et al developed Tempo, a reinforcement-learning based framework for deriving personalized screening policies. Tempo policies were significantly more efficient than existing clinical guidelines across diverse test sets from four hospitals.