Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.
Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.
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DOI：
10.1016/j.smhl.2018.01.002
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发表时间：
2018-06-01
期刊：
影响因子：
--
通讯作者：
Shah, Nirmish
中科院分区：
文献类型：
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作者：
Yang, Fan;Banerjee, Tanvi;Shah, Nirmish
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步。然而，疼痛是一种主观反应，很难通过客观方法进行评估。在本文中，我们提出了一个系统，使用机器学习技术将客观的生理指标映射到主观的自我报告的疼痛评分。使用多项逻辑回归和40例患者的数据，我们能够预测患者的疼痛评分在11点评定量表上，在个体内水平的平均准确度为0.578，在个体间水平的准确度为0.429。采用简化的4点评分量表，个体间水平的准确性进一步提高到0.681。总的来说，我们提出了一个初步的机器学习模型，可以预测SCD患者的疼痛评分，结果令人鼓舞。据我们所知，早期还没有通过在临床框架内利用机器学习概念在SCD或疼痛领域提出这样的系统。
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.