Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.
Abstract
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.
Cite
Text
Ali et al. "Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.." NeurIPS 2023 Workshops: XAIA, 2023.Markdown
[Ali et al. "Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.." NeurIPS 2023 Workshops: XAIA, 2023.](https://mlanthology.org/neuripsw/2023/ali2023neuripsw-explainable/)BibTeX
@inproceedings{ali2023neuripsw-explainable,
title = {{Explainable Reinforcement Learning for Alzheimer’s Disease Progression Prediction.}},
author = {Ali, Raja Farrukh and Farooq, Ayesha and Adeniji, Emmanuel and Woods, John and Sun, Vinny and Hsu, William},
booktitle = {NeurIPS 2023 Workshops: XAIA},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/ali2023neuripsw-explainable/}
}