Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.


Journal Article

Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.

Full Text

Duke Authors

Cited Authors

  • Yang, F; Banerjee, T; Narine, K; Shah, N

Published Date

  • June 2018

Published In

  • Smart Health (Amst)

Volume / Issue

  • 7-8 /

Start / End Page

  • 48 - 59

PubMed ID

  • 30906841

Pubmed Central ID

  • 30906841

International Standard Serial Number (ISSN)

  • 2352-6483

Digital Object Identifier (DOI)

  • 10.1016/j.smhl.2018.01.002


  • eng

Conference Location

  • Netherlands