使用临床文本测量镰状细胞病的疼痛
Measuring Pain in Sickle Cell Disease using Clinical Text
摘要：
镰状细胞病（SCD）是一种遗传性红细胞疾病。由于畸形的镰状红细胞通过小血管被捕获，因此SCD会发生疼痛、中风和器官衰竭等并发症。特别地，已知急性疼痛是SCD的主要症状。SCD疼痛的隐蔽性和主观性导致医生（MP）疼痛评估的挑战。因此，准确识别SCD患者的疼痛标志物对于疼痛管理至关重要。根据疼痛程度对SCD患者的临床记录进行分类，使MP能够给予适当的治疗。我们提出了一个二元分类模型来预测临床笔记的疼痛相关性和一个多类分类模型来预测疼痛水平。虽然我们的四个二进制机器学习（ML）分类器在性能上是相当的，但决策树在多类分类任务中的性能最好，F值达到0.70。我们的研究结果显示了潜在的临床文本分析和机器学习提供给镰状细胞患者的疼痛管理。
英文摘要：
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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