Yang Fan, Banerjee Tanvi, Narine Kalindi, Shah Nirmish
Department of Computer Science and Engineering, Wright State University, OH 45435, USA.
Department of Pediatrics, Division of Hematology and Oncology, Duke University Hospital, NC 27710, USA.
Smart Health (Amst). 2018 Jun;7-8:48-59. doi: 10.1016/j.smhl.2018.01.002. Epub 2018 Feb 2.
Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.
疼痛管理是镰状细胞病治疗的关键部分。准确的疼痛评估是疼痛管理的第一阶段。然而，疼痛是一种主观反应，难以通过客观方法进行评估。在本文中，我们提出了一种利用机器学习技术将客观生理指标映射到主观自我报告疼痛评分的系统。使用多项逻辑回归和来自40名患者的数据，我们能够在个体内水平上以平均0.578的准确率预测患者在11分制评分量表上的疼痛评分，在个体间水平上的准确率为0.429。采用简化的4分制评分量表时，个体间水平的准确率进一步提高到0.681。总体而言，我们提出了一个初步的机器学习模型，该模型能够预测镰状细胞病患者的疼痛评分，结果令人满意。据我们所知，在镰状细胞病或疼痛领域，此前尚未通过在临床框架内运用机器学习概念提出过这样的系统。
