Clinical applications of continual learning machine learning
With advances in artificial intelligence (AI), particularly in machine learning and deep
learning, the potential uses for AI in medicine are growing. Continual learning, also known …
learning, the potential uses for AI in medicine are growing. Continual learning, also known …
[HTML][HTML] Reinforcement learning for clinical decision support in critical care: comprehensive review
Background Decision support systems based on reinforcement learning (RL) have been
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …
A deep deterministic policy gradient approach to medication dosing and surveillance in the ICU
Medication dosing in a critical care environment is a complex task that involves close
monitoring of relevant physiologic and laboratory biomarkers and corresponding sequential …
monitoring of relevant physiologic and laboratory biomarkers and corresponding sequential …
A deep reinforcement learning approach for type 2 diabetes mellitus treatment
Z Liu, L Ji, X Jiang, W Zhao, X Liao… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Type 2 diabetes mellitus (T2DM) is a chronic disease that requires continuous treatments.
T2DM treatments aim to achieve not only short-term but, more importantly, long-term control …
T2DM treatments aim to achieve not only short-term but, more importantly, long-term control …
[HTML][HTML] Toward optimal heparin dosing by comparing multiple machine learning methods: retrospective study
Background: Heparin is one of the most commonly used medications in intensive care units.
In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice …
In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice …
Centroid distance distillation for effective rehearsal in continual learning
Rehearsal, retraining on a stored small data subset of old tasks, has been proven effective in
solving catastrophic forgetting in continual learning. However, due to the sampled data may …
solving catastrophic forgetting in continual learning. However, due to the sampled data may …
Deep Attention Q-Network for Personalized Treatment Recommendation
Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal
healthcare outcomes. Recent advances in reinforcement learning offer promising …
healthcare outcomes. Recent advances in reinforcement learning offer promising …
Systematic review of machine learning models for personalised dosing of heparin
N Falconer, A Abdel‐Hafez, IA Scott… - British Journal of …, 2021 - Wiley Online Library
Aim To identify and critically appraise studies of prediction models, developed using
machine learning (ML) methods, for determining the optimal dosing of unfractionated …
machine learning (ML) methods, for determining the optimal dosing of unfractionated …
Patient-specific sedation management via deep reinforcement learning
Introduction: Developing reliable medication dosing guidelines is challenging because
individual dose–response relationships are mitigated by both static (eg, demographic) and …
individual dose–response relationships are mitigated by both static (eg, demographic) and …
[HTML][HTML] Predicting therapeutic response to unfractionated heparin therapy: machine learning approach
A Abdel-Hafez, IA Scott, N Falconer, S Canaris… - Interactive journal of …, 2022 - i-jmr.org
Background: Unfractionated heparin (UFH) is an anticoagulant drug that is considered a
high-risk medication because an excessive dose can cause bleeding, whereas an …
high-risk medication because an excessive dose can cause bleeding, whereas an …