title: AIMI: Leveraging Future Knowledge and Personalization in Sparse Event
    Forecasting for Treatment Adherence
summary: 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.
paper_authors._id: 67dd4b64a412018fab289466
paper_authors.name: Abdullah Mamun
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-21T11:20:04.980Z
paper_authors._id: 657e623983543a061b585a9c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/657e623983543a061b585a9c/Zg9WWHQYPOtuYt9kDsq46.jpeg
paper_authors.fullname: Abdullah Mamun
paper_authors.type: user
paper_authors.user: ab9mamun
paper_authors._id: 67dd4b64a412018fab289467
paper_authors.name: Diane J. Cook
paper_authors._id: 67dd4b64a412018fab289468
paper_authors.name: Hassan Ghasemzadeh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T13:07:49.859Z
paper_authors._id: 64dcf2572057a9cd9ac1bc2e
paper_authors.avatarUrl: /avatars/ecbe33158bf7d854ac64666c5dee64a6.svg
paper_authors.fullname: Hassan Ghasemzadeh
paper_authors.type: user
paper_authors.user: HassanGM
paper_title: AIMI: Leveraging Future Knowledge and Personalization in Sparse Event
    Forecasting for Treatment Adherence
paper_summary: 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.
paper_ai_summary: A knowledge-guided adherence forecasting system using CNN and LSTM models leverages smartphone sensors and medication history to predict treatment adherence with high accuracy.
paper_ai_keywords: CNN LSTM adherence forecasting smartphone sensors medication history ablation studies convolutional neural networks recurrent neural networks