Explainable Reinforcement Learning for Alzheimer's Disease Progression Prediction.
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) …
interpretability of Reinforcement Learning (RL) models used for Alzheimer's Disease (AD) …
Explainable Reinforcement Learning for Alzheimer's Disease Progression Prediction: a SHAP-based Approach
In this study, we present a novel application of SHAP (SHapley Additive exPlanations) to
enhance the interpretability of Reinforcement Learning (RL) models for Alzheimer's Disease …
enhance the interpretability of Reinforcement Learning (RL) models for Alzheimer's Disease …
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction
Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease
(AD) progression due to its unique ability to model domain knowledge. However, it is not …
(AD) progression due to its unique ability to model domain knowledge. However, it is not …
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction
R Farrukh Ali, S Milani, J Woods, E Adenij… - arXiv e …, 2024 - ui.adsabs.harvard.edu
Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease
(AD) progression due to its unique ability to model domain knowledge. However, it is not …
(AD) progression due to its unique ability to model domain knowledge. However, it is not …
[图书][B] Interpretable and Explainable AI for Mental Health
N Wang - 2021 - search.proquest.com
Mental disorders cases are increasing around the world. Approximately, 1-in-7 of the
population worldwide are suffering from one or more mental or substance use disorders …
population worldwide are suffering from one or more mental or substance use disorders …
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention
in disease progression yet still demands attentive interpretability to explain how their DL …
in disease progression yet still demands attentive interpretability to explain how their DL …
Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer's disease detection
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability
to explain the complex decision-making process of machine learning (ML) and deep …
to explain the complex decision-making process of machine learning (ML) and deep …
An explainable AI paradigm for Alzheimer's diagnosis using deep transfer learning
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of
individuals worldwide, causing severe cognitive decline and memory impairment. The early …
individuals worldwide, causing severe cognitive decline and memory impairment. The early …
Reinforcement learning based disease progression model for Alzheimer's disease
We model Alzheimer's disease (AD) progression by combining differential equations (DEs)
and reinforcement learning (RL) with domain knowledge. DEs provide relationships …
and reinforcement learning (RL) with domain knowledge. DEs provide relationships …
An Explainable Deep Learning Model for Prediction of Severity of Alzheimer's Disease
Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical
imaging classification on various imaging modalities for binary and multiclass problems …
imaging classification on various imaging modalities for binary and multiclass problems …