Title: Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning,
Abstract: 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.