Measuring Pain in Sickle Cell Disease using Clinical Text

TL;DR

This paper develops machine learning models to classify clinical notes of Sickle Cell Disease patients based on pain relevance and level, aiding in better pain management through clinical text analysis.

Contribution

It introduces a binary and multiclass classification approach for pain assessment in SCD using clinical notes, demonstrating the effectiveness of ML models in this context.

Findings

Decision Trees achieved 0.70 F-measure in multiclass classification.

ML classifiers show potential for clinical pain assessment in SCD.

Binary classifiers performed comparably in identifying pain relevance.

Abstract

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.