Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning
This provides clinical decision support for low-resource settings, where surgical complications are higher, though it is incremental in using a simpler data source.
The researchers tackled the problem of predicting impending hypoxemia using only blood oxygenation data, achieving higher accuracy than trained anesthesiologists with full operating room data.
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.