基于社会认知理论，用多尺度模型预测乳腺癌患者长期服药行为。
A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach
- 结合动态与静态因素，构建日/周级服药行为预测模型。
- 日预测准确率87.25%，周预测准确率76.04%，优于传统方法。
- 揭示短期行为模式对每日依从性影响最大，长期因素影响每周趋势。
非依从性用药是慢性病管理中的关键问题，近半数患者未按医嘱服药，导致死亡率上升、医疗成本增加及可预防的身心痛苦。对于0-3期乳腺癌幸存者，坚持长期辅助内分泌治疗（如他莫昔芬和芳香化酶抑制剂）可显著提高无复发生存率。本文提出一种基于社会认知理论的计算框架，用于多尺度（日、周）纵向服药依从性建模。模型融合近期动态用药模式（动态因素）与相对稳定的个体特征（静态因素），以预测不同时间尺度下的依从性。结果显示，日级模型准确率达87.25%，周级模型为76.04%，均优于传统机器学习方法。动态用药历史对日级预测贡献最大，而周级预测需结合动态与静态因素共同解释。
原文摘要 · Abstract (English)
Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25%, and weekly models, an accuracy of 76.04%. Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns.
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