Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning

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 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.