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Pain is a subjective experience commonly measured through patient's selfreport. While there exist numerous situations in which automatic painestimation methods may be preferred, inter-subject variability in physiologicaland behavioral pain responses has hindered the development of such methods. Inthis work, we address this problem by introducing a novel personalizedmultitask machine learning method for pain estimation based on individualphysiological and behavioral pain response profiles, and show its advantages ina dataset containing multimodal responses to nociceptive heat pain.

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