Search Results for author:
Found 14 papers, 3 papers with code
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of significant distance and duration.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems.
Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs
Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making.
Understanding the Artificial Intelligence Clinician and optimal treatment strategies for sepsis in intensive care
In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community.
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
Model-Based Reinforcement Learning for Sepsis Treatment
Sepsis is a dangerous condition that is a leading cause of patient mortality.
Model-based Reinforcement Learning
reinforcement-learning
+1
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown.
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making
We capture this situation with partially observable Markov decision process, in which an agent optimises its actions in a belief represented as a distribution of patient states inferred from individual history trajectories.
Representation Balancing MDPs for Off-Policy Policy Evaluation
We study the problem of off-policy policy evaluation (OPPE) in RL.
Deep Reinforcement Learning for Sepsis Treatment
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually.
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning.
