乳腺癌幸存者的纵向药物坚持预测的计算框架:基于社会认知理论的方法
Navreet Kaur1, Manuel Gonzales Iv1, Cristian Garcia Alcaraz2
1Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
PLOS digital health
|June 10, 2025
概括
开发新的计算模型来预测乳腺癌幸存者的药物坚持,可以显著提高准确性. 这些模型使用过去的药物模式来预测坚持,帮助个性化干预和更好的患者结果.
科学领域:
- 计算建模计算建模
- 医疗信息学 医疗信息学
- 行为科学是一种行为科学.
背景情况:
- 药物不服药影响了近一半的慢性病患者,增加了死亡率和医疗保健成本.
- 坚持辅助内分泌疗法 (塔莫西芬,芳酶抑制剂) 对于乳腺癌幸存者的无复发生存至关重要 (阶段0-3).
研究的目的:
- 开发多尺度计算模型来预测纵向药物坚持.
- 了解动态和静态因素对每日和每周时间尺度的坚持的影响.
- 评估各种因素在预测坚持行为的重要性.
主要方法:
- 以社会认知理论为指导的计算框架被用于多尺度 (每日和每周) 建模.
- 模型结合了动态的过去药物服用模式和静态因素来预测坚持.
- 模型性能与传统的机器学习方法进行了比较.
主要成果:
- 与传统方法相比,多尺度模型显示出更高的准确性和特异性.
- 每天的模型达到了87.25%的准确性 (92.04%的精度,93.15%的回忆,77.50%的特异性).
- 每周的模型达到了76.04%的准确度 (75.83%精度,85.80%回忆,72.30%的特异性).
- 动态过去的药物使用模式是每日预测的关键;动态和静态因素的混合对每周预测具有重要意义.
结论:
- 一种基于理论的多尺度方法有效地预测了药物坚持.
- 这些发现支持个性化,技术驱动的坚持干预措施的设计.
- 未来的工作包括外部验证和探索丰富的传感器系统,以评估合规性.
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