Citation:
Lopez-Martinez D, Rudovic O, Picard R. Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning, in Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Health. Long Beach, USA ; 2017.
Abstract:
Pain is a subjective experience commonly measured through patient's selfreport. 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.