TY - JOUR AU - Rahman, Quazi Abidur AU - Janmohamed, Tahir AU - Pirbaglou, Meysam AU - Clarke, Hance AU - Ritvo, Paul AU - Heffernan, Jane M AU - Katz, Joel PY - 2018 DA - 2018/11/15 TI -在管理我的疼痛应用程序的用户中定义和预测疼痛波动:使用数据挖掘和机器学习方法分析JO - J Med Internet Res SP - e12001 VL - 20 IS - 11kw -慢性疼痛KW -疼痛波动KW -数据挖掘KW -聚类分析KW -机器学习KW -预测模型KW -管理我的疼痛KW -疼痛应用程序AB -背景:测量和预测疼痛波动(疼痛评分随时间的波动或变化)可以帮助改善疼痛管理。在与疼痛波动相关的更大的不确定性和不可预测性的条件下，对疼痛及其随之而来的致残影响的感知往往会增强。目的:本研究旨在使用数据挖掘和机器学习方法(1)定义一种新的疼痛波动率测量方法;(2)基于人口统计学、临床和应用程序使用特征，从疼痛管理应用程序Manage My pain的用户中预测未来的疼痛波动率水平。方法:疼痛波动率定义为观察期内连续2个自我报告的疼痛严重程度评分之间绝对变化的平均值。将k-means聚类算法应用于用户在应用程序使用的第一个月和第六个月的疼痛波动率评分，以建立区分低波动率和高波动率类别的阈值。随后，我们从应用程序使用的第一个月提取了130个人口统计学、临床和应用程序使用特征，以预测应用程序使用的第六个月的这两个波动类别。采用4种方法建立预测模型:(1)带岭估计的逻辑回归;(2)基于最小绝对收缩和选择算子的逻辑回归;(3)随机森林; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. Results: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. Conclusions: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month. SN - 1438-8871 UR - //www.mybigtv.com/2018/11/e12001/ UR - https://doi.org/10.2196/12001 UR - http://www.ncbi.nlm.nih.gov/pubmed/30442636 DO - 10.2196/12001 ID - info:doi/10.2196/12001 ER -