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

2025-03-20

AI Summary

To address the challenges of low predictive accuracy and lack of real-time intervention for medication adherence in chronic disease management, this paper proposes a knowledge-guided personalized prediction system. Methodologically, it introduces the first integration of prospective temporal knowledge—such as scheduled medication times—with individual medication history and smartphone sensor time-series data, implemented via an LSTM-CNN multimodal architecture. Knowledge injection, domain-informed feature engineering, and user-specific fine-tuning collectively enhance robustness and interpretability for short-term prediction under sparse-event conditions. Evaluated on 27 cardiovascular patients, the system achieves 93.2% accuracy and 93.6% F1-score, significantly outperforming baseline models. Key contributions include: (i) incorporation of prospective temporal knowledge to strengthen sequential modeling; and (ii) development of a lightweight, interpretable, edge-deployable prediction framework enabling just-in-time interventions.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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.

Problem

• Forecasting treatment adherence using wearable sensors and smartphone data.
• Developing personalized intervention tools for chronic condition patients.
• Enhancing medication adherence prediction with machine learning models.

Innovation

• Leverages smartphone sensors for adherence forecasting
• Uses LSTM models for high accuracy predictions
• Incorporates future knowledge and personalization in training