使用机器学习方法预测青少年多部位疼痛发生率：一项为期两年的纵向研究。

Prediction of Multisite Pain Incidence in Adolescence Using a Machine Learning Approach: A 2-Year Longitudinal Study.

作者信息

Joensuu Laura, Rautiainen Ilkka, Hautala Arto J, Siekkinen Kirsti, Pirnes Katariina, Tammelin Tuija H

机构信息

Faculty of Sport and Health Sciences University of Jyväskylä Jyväskylä Finland.

Likes Jamk University of Applied Sciences Jyväskylä Finland.

出版信息

Health Sci Rep. 2024 Dec 10;7(12):e70252. doi: 10.1002/hsr2.70252. eCollection 2024 Dec.

DOI:10.1002/hsr2.70252

PMID:39659818

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11628870/

Abstract

BACKGROUND AND AIMS

Multisite pain is a prevalent and significant issue among adolescents, often associated with adverse physical, psychological, and social outcomes. We aimed to (1) predict multisite pain incidence in the whole body and in the musculoskeletal sites in adolescents, and (2) explore the sex-specific predictors of multisite pain incidence using a novel machine learning (ML) approach (random forest, AdaBoost, and support vector classifier).

METHODS

A 2-year longitudinal observational study (2013-2015) was conducted in a population-based sample of Finnish adolescents ( = 410, 57% girls, 12.5 years (SD = 1.2) at baseline). Three different data sets were used. First data included 48 pre-selected variables relevant for adolescents' health and wellbeing. The second data included nine physical fitness variables related to the Finnish national 'Move!' monitoring system for health-related fitness. The third data set included all available baseline data (392 variables). Multisite pain was self-reported weekly pain during the past 3 months manifesting in at least three sites and not related to any known disease or injury. Musculoskeletal pain sites included the neck/shoulder, upper extremities, chest, upper back, low back, buttocks, and lower extremities. Whole body pain sites also included the head and abdominal areas.

RESULTS

Overall, 16% of boys and 28% of girls developed multisite pain in the whole body and 10% and 15% in the musculoskeletal area during the 2-year follow-up. The prediction ability of ML reached area under the receiver operating characteristic curve 0.78 at highest but remained mainly < 0.7 for the majority of the methods. With ML, a broad variety of predictors were identified, with up to 33 variables showing predictive power in girls and 13 in boys.

CONCLUSION

The results highlight that rather than any isolated variable, a variety of factors contribute to future multisite pain.

摘要

背景与目的

多部位疼痛在青少年中是一个普遍且严重的问题，常与不良的身体、心理和社会后果相关。我们旨在：（1）预测青少年全身及肌肉骨骼部位多部位疼痛的发生率；（2）使用一种新型机器学习（ML）方法（随机森林、自适应增强和支持向量分类器）探索多部位疼痛发生率的性别特异性预测因素。

方法

在芬兰青少年的基于人群的样本中进行了一项为期2年的纵向观察性研究（2013 - 2015年）（n = 410，57%为女孩，基线时年龄12.5岁（标准差 = 1.2））。使用了三个不同的数据集。第一个数据集包括48个与青少年健康和幸福相关的预先选定变量。第二个数据集包括与芬兰国家“运动！”健康相关体能监测系统相关的9个体能变量。第三个数据集包括所有可用的基线数据（392个变量）。多部位疼痛是指过去3个月内每周自我报告的疼痛，至少出现在三个部位，且与任何已知疾病或损伤无关。肌肉骨骼疼痛部位包括颈部/肩部、上肢、胸部、上背部、下背部、臀部和下肢。全身疼痛部位还包括头部和腹部区域。

结果

总体而言，在2年的随访期间，16%的男孩和28%的女孩出现了全身多部位疼痛，肌肉骨骼区域分别为10%和15%。ML的预测能力在最高时达到受试者工作特征曲线下面积为0.78，但大多数方法主要仍<0.7。通过ML，识别出了各种各样的预测因素，多达33个变量在女孩中显示出预测能力，13个在男孩中显示出预测能力。