Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease.

Journal Article (Journal Article)

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient's subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients' pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain.

Full Text

Duke Authors

Cited Authors

  • Panaggio, MJ; Abrams, DM; Yang, F; Banerjee, T; Shah, NR

Published Date

  • March 2021

Published In

Volume / Issue

  • 17 / 3

Start / End Page

  • e1008542 -

PubMed ID

  • 33705373

Pubmed Central ID

  • PMC7951914

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1008542


  • eng

Conference Location

  • United States