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 …
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 …
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 …
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 …
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 …
Machine learning based multi-modal prediction of future decline toward Alzheimer's disease: an empirical study
Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades.
Early detection of individuals at high risk of future progression toward AD is likely to be of …
Early detection of individuals at high risk of future progression toward AD is likely to be of …
A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data
C Wang, Y Li, Y Tsuboshita, T Sakurai, T Goto… - NPJ digital …, 2022 - nature.com
Alzheimer's disease is a neurodegenerative disease that imposes a substantial financial
burden on society. A number of machine learning studies have been conducted to predict …
burden on society. A number of machine learning studies have been conducted to predict …
Predicting Alzheimer's disease progression trajectory and clinical subtypes using machine learning
Background Alzheimer's disease (AD) is a common, age-related, neurodegenerative
disease that impairs a person's ability to perform day to day activities. Diagnosing AD is …
disease that impairs a person's ability to perform day to day activities. Diagnosing AD is …
Machine learning for comprehensive forecasting of Alzheimer's Disease progression
Most approaches to machine learning from electronic health data can only predict a single
endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial …
endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial …