Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning
This addresses the need for automatic pain estimation methods in medical or clinical settings, though it appears incremental as it builds on existing multitask learning approaches.
The paper tackled the problem of inter-subject variability in pain responses by introducing a personalized multitask learning method for pain estimation, showing advantages in a dataset with multimodal responses to nociceptive heat pain.
Pain is a subjective experience commonly measured through patient's self report. While there exist numerous situations in which automatic pain estimation methods may be preferred, inter-subject variability in physiological and behavioral pain responses has hindered the development of such methods. In this work, we address this problem by introducing a novel personalized multitask machine learning method for pain estimation based on individual physiological and behavioral pain response profiles, and show its advantages in a dataset containing multimodal responses to nociceptive heat pain.