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

Measuring Pain in Sickle Cell Disease using Clinical Text