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Leveraging Explainability of Deep Reinforcement Learning in Healthcare: The Case of Type 1 Diabetes Management

https://doi.org/10.2139/ssrn.5375341
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inferredcites →https://doi.org/10.1007/978-3-030-37446-4_9openalex-snapshot
Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening
inferredcites →https://doi.org/10.1007/s11892-013-0433-5openalex-snapshot
The Emerging Global Epidemic of Type 1 Diabetes
inferredcites →https://doi.org/10.1007/s41666-022-00114-1openalex-snapshot
Explainable Artificial Intelligence for Predictive Modeling in Healthcare
inferredcites →https://doi.org/10.1016/j.arcontrol.2019.07.004openalex-snapshot
The evolution of control algorithms in artificial pancreas: A historical perspective
inferredcites →https://doi.org/10.1016/j.future.2024.01.021openalex-snapshot
Reinforcement learning-based dynamic pruning for distributed inference via explainable AI in healthcare IoT systems
inferredcites →https://doi.org/10.1016/j.jbi.2023.104376openalex-snapshot
Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
inferredcites →https://doi.org/10.1016/j.jneumeth.2013.10.018openalex-snapshot
The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference
inferredcites →https://doi.org/10.1016/j.tem.2019.04.008openalex-snapshot
A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control
inferredcites →https://doi.org/10.1016/s1574-0005(02)03016-3openalex-snapshot
Chapter 53 The shapley value
inferredcites →https://doi.org/10.1017/s0269888921000102openalex-snapshot
Contrastive explanation: a structural-model approach
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1080/00949655.2018.1505197openalex-snapshot
Elements of causal inference: foundations and learning algorithms
inferredcites →https://doi.org/10.1089/dia.2009.0076openalex-snapshot
Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring
inferredcites →https://doi.org/10.1109/access.2021.3100007openalex-snapshot
A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation
inferredcites →https://doi.org/10.1109/access.2023.3259425openalex-snapshot
Reinforcement Learning Models and Algorithms for Diabetes Management
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9533957openalex-snapshot
Reinforcement Learning-Based Insulin Injection Time And Dosages Optimization
inferredcites →https://doi.org/10.1109/jbhi.2019.2931842openalex-snapshot
GluNet: A Deep Learning Framework for Accurate Glucose Forecasting
inferredcites →https://doi.org/10.1109/jbhi.2020.3014556openalex-snapshot
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation
inferredcites →https://doi.org/10.1109/metroxraine62247.2024.10796610openalex-snapshot
Interpreting Type 1 Diabetes Management via Contrastive Explanations
inferredcites →https://doi.org/10.1109/rbme.2023.3331297openalex-snapshot
Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities
inferredcites →https://doi.org/10.1109/tcyb.2021.3049630openalex-snapshot
Deep-LIFT: Deep Label-Specific Feature Learning for Image Annotation
inferredcites →https://doi.org/10.1109/tpami.2021.3065601openalex-snapshot
Neural Granger Causality
inferredcites →https://doi.org/10.1145/3477600openalex-snapshot
Reinforcement Learning in Healthcare: A Survey
inferredcites →https://doi.org/10.1145/3616864openalex-snapshot
Explainable Reinforcement Learning: A Survey and Comparative Review
inferredcites →https://doi.org/10.1177/1932296813514502openalex-snapshot
The UVA/PADOVA Type 1 Diabetes Simulator
inferredcites →https://doi.org/10.1214/11-aos940openalex-snapshot
Learning high-dimensional directed acyclic graphs with latent and selection variables
inferredcites →https://doi.org/10.1609/aaai.v34i05.6220openalex-snapshot
Shapley Q-Value: A Local Reward Approach to Solve Global Reward Games
inferredcites →https://doi.org/10.1613/jair.301openalex-snapshot
Reinforcement Learning: A Survey
inferredcites →https://doi.org/10.2196/10775openalex-snapshot
Artificial Intelligence for Diabetes Management and Decision Support: Literature Review
inferredcites →https://doi.org/10.2196/50328openalex-snapshot
A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study
inferredcites →https://doi.org/10.2307/1912791openalex-snapshot
Investigating Causal Relations by Econometric Models and Cross-spectral Methods
inferredcites →https://doi.org/10.25080/tcwv9851openalex-snapshot
Exploring Network Structure, Dynamics, and Function using NetworkX
inferredcites →https://doi.org/10.3233/faia251067openalex-snapshot
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback
inferredcites →https://doi.org/10.3390/diagnostics13193150openalex-snapshot
Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes
inferredcites →https://doi.org/10.3390/make5040072openalex-snapshot
Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1412.3773openalex-snapshot
Distinguishing cause from effect using observational data: methods and benchmarks
inferredcites →https://doi.org/10.48550/arxiv.1704.02685openalex-snapshot
Learning Important Features Through Propagating Activation Differences
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.2009.09051openalex-snapshot
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control
inferredcites →https://doi.org/10.48550/arxiv.2202.08434openalex-snapshot
A Survey of Explainable Reinforcement Learning
inferredcites →https://doi.org/10.52202/068431-0430openalex-snapshot
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
inferredcites →https://doi.org/10.7551/mitpress/1754.001.0001openalex-snapshot
Causation, Prediction, and Search
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