We use a deep learning model trained only on a patient's blood oxygenation data (measurable with an inexpensive fingertip sensor) to predict impending hypoxemia (low blood oxygen) more accurately than trained anesthesiologists with access to all the data recorded in a modern operating room. We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values. This work has the potential to provide cutting-edge clinical decision support in low-resource settings, where rates of surgical complication and death are substantially greater than in high-resource areas.
@article{arxiv.1712.00563,
title = {Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning},
author = {Gabriel Erion and Hugh Chen and Scott M. Lundberg and Su-In Lee},
journal= {arXiv preprint arXiv:1712.00563},
year = {2017}
}
Comments
To be presented at Machine Learning for Health Workshop: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA