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