# Measuring Pain in Sickle Cell Disease using Clinical Text

arXiv: https://arxiv.org/abs/2008.11081 · HTML: https://mlsanity.com/paper/2008.11081/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Computational analysis of the language of pain: a systematic review](https://mlsanity.com/paper/2404.16226/) (2024)
- [PLeDO: Pain Level Detection for Osteoarthritis from EMR Data](https://mlsanity.com/paper/2608.15719/) (2026)

## Abstract (arXiv)

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.