AIMI：利用未来知识与个性化技术进行稀疏事件预测以提升治疗依从性

AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence

March 20, 2025

作者: Abdullah Mamun, Diane J. Cook, Hassan Ghasemzadeh

cs.AI

摘要

对于慢性病患者而言，遵循既定治疗方案至关重要，以避免高昂或不良的健康后果。针对特定患者群体，强化生活方式干预对提升药物依从性尤为关键。准确预测治疗依从性，能够为开发按需干预工具铺平道路，实现及时且个性化的支持。随着智能手机和可穿戴设备的日益普及，开发并部署智能活动监测系统变得前所未有的便捷。然而，基于可穿戴传感器的有效治疗依从性预测系统仍未广泛普及。我们通过提出“基于机器智能的依从性预测与干预系统”（AIMI）填补了这一空白。AIMI是一个知识引导的依从性预测系统，它利用智能手机传感器及既往用药记录，来估算患者忘记服用处方药物的可能性。我们开展了一项用户研究，涉及27名每日服药以管理心血管疾病的参与者。我们设计并开发了基于CNN和LSTM的预测模型，结合多种输入特征组合，发现LSTM模型能以0.932的准确率和0.936的F1分数预测药物依从性。此外，通过一系列涉及卷积神经网络和循环神经网络架构的消融研究，我们证明了利用未来已知信息及个性化训练能显著提升药物依从性预测的准确性。代码已公开：https://github.com/ab9mamun/AIMI。

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.