Artificial intelligence and machine learning for improving glycemic control in diabetes: Best practices, pitfalls, and opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
Probabilistic machine learning for healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …
learning models help provide a complete picture of observed data in healthcare. In this …
The effects of reward misspecification: Mapping and mitigating misaligned models
Reward hacking--where RL agents exploit gaps in misspecified reward functions--has been
widely observed, but not yet systematically studied. To understand how reward hacking …
widely observed, but not yet systematically studied. To understand how reward hacking …
[HTML][HTML] Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
The widespread adoption of effective hybrid closed loop systems would represent an
important milestone of care for people living with type 1 diabetes (T1D). These devices …
important milestone of care for people living with type 1 diabetes (T1D). These devices …
Online reinforcement learning for a continuous space system with experimental validation
Reinforcement learning (RL) for continuous state/action space systems has remained a
challenge for nonlinear multivariate dynamical systems even at a simulation level …
challenge for nonlinear multivariate dynamical systems even at a simulation level …
Reinforcement learning with state observation costs in action-contingent noiselessly observable markov decision processes
HJA Nam, S Fleming… - Advances in Neural …, 2021 - proceedings.neurips.cc
Many real-world problems that require making optimal sequences of decisions under
uncertainty involve costs when the agent wishes to obtain information about its environment …
uncertainty involve costs when the agent wishes to obtain information about its environment …
Offline deep reinforcement learning and off-policy evaluation for personalized basal insulin control in type 1 diabetes
Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas
(AP), have been shown to optimize glucose control and reduce the self-management …
(AP), have been shown to optimize glucose control and reduce the self-management …
Responsible and regulatory conform machine learning for medicine: a survey of challenges and solutions
E Petersen, Y Potdevin, E Mohammadi… - IEEE …, 2022 - ieeexplore.ieee.org
Machine learning is expected to fuel significant improvements in medical care. To ensure
that fundamental principles such as beneficence, respect for human autonomy, prevention of …
that fundamental principles such as beneficence, respect for human autonomy, prevention of …
Mealtime prediction using wearable insulin pump data to support diabetes management
Many patients with diabetes struggle with post-meal high blood glucose due to missed or
untimely meal-related insulin doses. To address this challenge, our research aims to:(1) …
untimely meal-related insulin doses. To address this challenge, our research aims to:(1) …
Reinforcement learning for patient-specific optimal stenting of intracranial aneurysms
Developing new capabilities to predict the risk of intracranial aneurysm rupture and to
improve treatment outcomes in the follow-up of endovascular repair is of tremendous …
improve treatment outcomes in the follow-up of endovascular repair is of tremendous …