@inproceedings{
ali2023explainable,
title={Explainable Alzheimer{\textquoteright}s Disease Progression Prediction using Reinforcement Learning},
author={Raja Farrukh Ali and Ayesha Farooq and Emmanuel Adeniji and John Woods and Vinny Sun and William Hsu},
booktitle={XAI in Action: Past, Present, and Future Applications},
year={2023},
url={https://openreview.net/forum?id=joaWGug1CU}
}
We present a novel application of SHAP (SHapley Additive exPlanations) to enhance the interpretability of Reinforcement Learning (RL) models used for Alzheimer's Disease (AD) progression prediction. Leveraging RL's predictive capabilities on a subset of the ADNI dataset, we employ SHAP to explain the model's decision-making process. Our approach provides detailed insights into the key factors influencing AD progression predictions, offering both global and individual, patient-level interpretability. By bridging the gap between predictive power and transparency, our work is a step towards empowering clinicians and researchers to gain a deeper understanding of AD progression and facilitate more informed decision-making in AD-related research and patient care. To encourage further exploration, we  open-source our codebase at https://github.com/rfali/xrlad.
Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.
Raja Farrukh Ali Ayesha Farooq Emmanuel Adeniji John Woods Vinny Sun William Hsu