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

Mark J. Panaggio*, Daniel M. Abrams, Fan Yang, Tanvi Banerjee, Nirmish R. Shah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere1008542
JournalPLoS computational biology
Volume17
Issue number3
DOIs
StatePublished - Mar 11 2021

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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