@inproceedings{
flashner2023learning,
title={Learning Expert-Interpretable Programs for Myocardial Infarction Localization},
author={Joshua Flashner and Jennifer Sun and David Ouyang and Yisong Yue},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=zXhQnoaRQW}
}
We study how to learn accurate and interpretable models for assisted clinical diagnostics. We focus on myocardial infarction (heart attack) localization from electrocardiogram (ECG) signals, which is known to have a complex mapping that is challenging even for expert cardiologists to understand. Our approach leverages recent advances in learning neurosymbolic models, and yields inherently expert interpretable programs as compositions of ECG features and learned temporal filters. We evaluate our method on a set of 21,844 ECG recordings, to localize myocardial infarction at different levels of granularity. Results demonstrate that our model performs comparably to conventional black-box baselines, but with a much simpler and more interpretable structure.
Learning Expert-Interpretable Programs for Myocardial Infarction Localization
Joshua Alan Flashner Jennifer J. Sun David Ouyang Yisong Yue