Pain is commonly evaluated through verbal patient self-report, and these assessments are often considered the gold standard, but due to their subjective nature, they may not be reliable. This episode explores two machine learning models for pain assessment, utilizing behavioral and physiological indicators to help doctors determine this symptom, even in non-verbal patients.

References

1. Prkachin KM, Hammal Z. Computer mediated automatic detection of pain-related behavior: prospect, progress, perils. Front Pain Res (Lausanne). [Internet] 2021. (Accessed on Jun 8, 2022);1;2:788606. Available at: https://www.frontiersin.org/articles/10.3389/fpain.2021.788606/full
2. Pouromran F, Radhakrishnan S, Kamarthi S. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLoS One. [Internet] 2021. (Accessed on Jun 8, 2022);16(7):e0254108. Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0254108
3. Wu CL, Liu SF, Yu TL, Shih SJ, Chang CH, Yang Mao SF, Li YS, Chen HJ, Chen CC, Chao WC. Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients. Front Med (Lausanne). [Internet] 2022. (Accessed on Jun 8, 2022);9:851690. Available at: https://www.frontiersin.org/articles/10.3389/fmed.2022.851690/full

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