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关于纤维肌痛的网络搜索趋势:开发机器学习模型

Matteo Luigi G Leoni¹, Marco Mercieri², Antonella Paladini³

¹Department of Medical and Surgical Sciences and Translational Medicine, Sapienza University of Rome, Italy. matteolg.leoni@gmail.com.

Clinical and experimental rheumatology

|June 10, 2025

概括

在线搜索纤维肌痛 (FM) 在全球范围内有所不同. 最近的搜索历史记录显著预测了未来的兴趣,突出了针对性数字健康教育和宣传活动的机会.

科学领域:

* 数字健康数字健康
* 医疗信息学医疗信息学
* 慢性疼痛研究研究

背景情况:

*纤维肌痛 (FM) 是一种复杂的慢性疼痛疾病,影响着全球数百万人.
*互联网是健康信息的主要来源,影响患者的理解和参与.
*分析在线搜索行为为FM提供了对公众利益和信息搜索模式的见解.

研究的目的:

*分析2020-2024年纤维肌痛 (FM) 的国际在线搜索趋势.
* 用统计和机器学习模型来识别时间模式并预测未来的搜索兴趣.
* 评估在线搜索对FM的兴趣的主要驱动因素.

主要方法:

*使用了来自16个国家的谷歌趋势数据 (2020-2024年).
*采用时间序列分析,线性回归,曼-肯达尔趋势测试和季节性分解.
* 应用自动回归集成移动平均 (ARIMA),随机森林 (RF) 和极端梯度提升 (XGBoost) 模型,具有特征重要性的SHAP值.

主要成果:

*不同国家对FM搜索兴趣的差异很大;中国,英国,美国和加拿大参与度最高.
*在巴西,意大利和英国观察到上升趋势;在阿根廷,加拿大,希腊和美国观察到下降趋势.
* 短期搜索历史 (前一天,一周,一个月) 是机器学习模型中最有影响力的预测因素.

结论:

*在线搜索行为分析为改善纤维肌痛 (FM) 患者教育提供了宝贵的见解.
*有针对性的数字健康素养倡议和宣传活动可以提高参与度和知识.
*优化在线医疗资源和决策辅助工具对于未来的患者支持至关重要.

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