  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.
