Measuring Pain in Sickle Cell Disease using Clinical Text

Conference Paper

Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans. Complications such as pain, stroke, and organ failure occur in SCD as malformed, sickled red blood cells passing through small blood vessels get trapped. Particularly, acute pain is known to be the primary symptom of SCD. The insidious and subjective nature of SCD pain leads to challenges in pain assessment among Medical Practitioners (MPs). Thus, accurate identification of markers of pain in patients with SCD is crucial for pain management. Classifying clinical notes of patients with SCD based on their pain level enables MPs to give appropriate treatment. We propose a binary classification model to predict pain relevance of clinical notes and a multiclass classification model to predict pain level. While our four binary machine learning (ML) classifiers are comparable in their performance, Decision Trees had the best performance for the multiclass classification task achieving 0.70 in F-measure. Our results show the potential clinical text analysis and machine learning offer to pain management in sickle cell patients.

Full Text

Duke Authors

Cited Authors

  • Alambo, A; Andrew, R; Gollarahalli, S; Vaughn, J; Banerjee, T; Thirunarayan, K; Abrams, D; Shah, N

Published Date

  • July 1, 2020

Published In

Volume / Issue

  • 2020-July /

Start / End Page

  • 5838 - 5841

International Standard Serial Number (ISSN)

  • 1557-170X

International Standard Book Number 13 (ISBN-13)

  • 9781728119908

Digital Object Identifier (DOI)

  • 10.1109/EMBC44109.2020.9175599

Citation Source

  • Scopus