@conference {582271,
	title = {Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning},
	booktitle = {Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Health},
	year = {2017},
	address = {Long Beach, USA},
	abstract = {Pain is a subjective experience commonly measured through patient{\textquoteright}s self\&nbsp;report. While there exist numerous situations in which automatic pain\&nbsp;estimation methods may be preferred, inter-subject variability in physiological\&nbsp;and behavioral pain responses has hindered the development of such methods. In\&nbsp;this work, we address this problem by introducing a novel personalized\&nbsp;multitask machine learning method for pain estimation based on individual\&nbsp;physiological and behavioral pain response profiles, and show its advantages in\&nbsp;a dataset containing multimodal responses to nociceptive heat pain.\&nbsp;},
	author = {Lopez-Martinez, Daniel and Rudovic, Ognjen and Picard, Rosalind}
}
