AIMI：利用未來知識與個人化於稀疏事件預測中提升治療依從性

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

對於慢性病患者而言，遵循醫囑治療至關重要，以避免高昂或不良的健康後果。對於某些患者群體，強化生活方式干預是提升藥物依從性的關鍵。準確預測治療依從性能夠為開發按需干預工具開闢道路，從而提供及時且個性化的支持。隨著智能手機和可穿戴設備的日益普及，開發和部署智能活動監測系統變得前所未有的便捷。然而，基於可穿戴傳感器的有效治療依從性預測系統仍未廣泛普及。我們通過提出「基於機器智能的依從性預測與干預系統」（Adherence Forecasting and Intervention with Machine Intelligence, AIMI）來彌補這一空白。AIMI是一個知識引導的依從性預測系統，它利用智能手機傳感器和既往用藥歷史來估計患者忘記服用處方藥物的可能性。我們對27名每日服藥以管理心血管疾病的參與者進行了用戶研究。我們設計並開發了基於CNN和LSTM的預測模型，結合多種輸入特徵，發現LSTM模型能夠以0.932的準確率和0.936的F1分數預測藥物依從性。此外，通過一系列涉及卷積和循環神經網絡架構的消融研究，我們證明了利用已知的未來信息和個性化訓練能夠提升藥物依從性預測的準確性。代碼可於以下網址獲取：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.