用规范后勤回归和轻GBM:基于网络的调查分析来确定影响一般患者药物遵守因素的相对重要性
Haru Iino1, Hayato Kizaki1, Shungo Imai1
1Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, Tokyo, Japan.
JMIR formative research
|December 23, 2024
概括
一致的药物治疗和食时间是遵守药物治疗的关键. 生活习惯显著影响患者的坚持,突出了机器学习对分析复杂患者坚持因素的有用性.
科学领域:
- 药理学和卫生服务研究 研究 药理学和卫生服务研究
- 医疗保健中的机器学习
- 患者坚持性研究
背景情况:
- 药物服药受到心理,行为和人口因素的影响.
- 多对线性和变量选择在分析这些因素时带来了挑战.
- 机器学习方法为解决多对线性问题和评估因素重要性提供了解决方案.
研究的目的:
- 为了确定影响药物遵守的关键因素.
- 应用规范后勤回归和LightGBM进行分析.
主要方法:
- 一项针对日本638名成年患者的问卷调查,他们服用药物超过3个月.
- 收集有关人口统计,药物习惯,心理因素和合规性的数据.
- 使用调节后勤回归用于多对线性和LightGBM用于特征重要性.
主要成果:
- 规范化后勤回归确定了药物和饮食摄入的一致时间,以及减少药物服用的愿望作为显著预测因素.
- 轻GBM显示"年龄"和"每天大约在同一时间使用药物"是主要因素.
- 在坚持因素组中,与生活方式相关的项目显示了最高的特征重要性 (77.92).
结论:
- 一致的药物治疗和食时间对于药物服药至关重要.
- 生活习惯是对药物遵守的最重要的贡献者.
- 像LightGBM这样的规范化和机器学习模型有效地分析受多对线性影响的坚持因素.
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