用手机传感器和用药史预测忘吃药概率，实现个性化提醒。
AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence
- 结合手机数据与历史用药记录，用LSTM模型预测服药行为。
- 在27人研究中准确率达93.2%，F1分数达93.6%。
- 融入未来知识与个体差异，适合慢病管理智能干预场景。
慢性病患者按时服药对避免严重健康后果至关重要。针对特定人群，强化生活方式干预可显著提升服药依从性。精准预测服药依从性可推动按需干预工具的开发，实现及时个性化支持。随着智能手机和可穿戴设备普及，智能活动监测系统已具备可行性。然而，基于可穿戴传感器的有效服药依从性预测系统仍不常见。本文提出自适应依从性预测与智能干预系统（AIMI），该系统融合手机传感器数据与既往用药史，评估忘服药概率。在27名心血管疾病患者参与的用户研究中，设计并开发了多种输入特征组合的CNN与LSTM模型，结果显示LSTM模型在服药依从性预测上达到0.932的准确率与0.936的F1分数。通过一系列消融实验，验证了引入未来知识与个性化训练能显著提升预测精度。代码已公开：https://github.com/ab9mamun/AIMI。
原文摘要 · Abstract (English)
Adherence to prescribed treatments is crucial for individuals with chronic conditions to avoid costly or adverse health outcomes. For certain patient groups, intensive lifestyle interventions are vital for enhancing medication adherence. Accurate forecasting of treatment adherence can open pathways to developing an on-demand intervention tool, enabling timely and personalized support. With the increasing popularity of smartphones and wearables, it is now easier than ever to develop and deploy smart activity monitoring systems. However, effective forecasting systems for treatment adherence based on wearable sensors are still not widely available. We close this gap by proposing Adherence Forecasting and Intervention with Machine Intelligence (AIMI). AIMI is a knowledge-guided adherence forecasting system that leverages smartphone sensors and previous medication history to estimate the likelihood of forgetting to take a prescribed medication. A user study was conducted with 27 participants who took daily medications to manage their cardiovascular diseases. We designed and developed CNN and LSTM-based forecasting models with various combinations of input features and found that LSTM models can forecast medication adherence with an accuracy of 0.932 and an F-1 score of 0.936. Moreover, through a series of ablation studies involving convolutional and recurrent neural network architectures, we demonstrate that leveraging known knowledge about future and personalized training enhances the accuracy of medication adherence forecasting. Code available: https://github.com/ab9mamun/AIMI.
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