Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals
This addresses pain assessment challenges for patients who cannot communicate verbally, though it appears incremental as it builds on existing multi-task learning approaches.
The researchers tackled the problem of measuring pain intensity when patients cannot self-report by developing a multi-task neural network method using physiological signals, achieving results that account for individual differences while leveraging population data.
Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.