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Query: UMLS:C0184567 (acute pain)
 3,962 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Haemoglobin SC (HbSC) disease accounts for 30% of cases of sickle cell disease in the United Kingdom and the United States. Unlike other sickle cell carriers, who are relatively asymptomatic, people with HbSC disease have a combination of genotypes with the potential to cause considerable morbidity due to intracellular water loss. Patients can present with acute pain, acute chest syndrome, proliferative retinopathy, splenic and renal complications, or stroke. We present a young man with HbSC disease who developed acute compartment syndrome. This is only the second report of this syndrome in a patient with HbSC disease. This is a very rare complication in HbSC disease, but it can have serious implications.
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PMID:Acute compartment syndrome in a patient with sickle cell disease. 3277 27

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.
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PMID:Measuring Pain in Sickle Cell Disease using Clinical Text. 3301 1


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