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

arXiv: https://arxiv.org/abs/1711.04036 · HTML: https://mlsanity.com/paper/1711.04036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors](https://mlsanity.com/paper/2407.19475/) (2024)
- [Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices](https://mlsanity.com/paper/2209.01813/) (2022)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)

## Abstract (arXiv)

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.