In-Silico Deep Reinforcement Learning for Effective Cardiac Ablation Strategy

2021 ◽  
Author(s):  
Hiroshi Seno ◽  
Masatoshi Yamazaki ◽  
Nitaro Shibata ◽  
Ichiro Sakuma ◽  
Naoki Tomii
Keyword(s):  
Reinforcement Learning ◽  
In Silico ◽  
Cardiac Ablation

scholarly journals B-PO03-024 IN-SILICO DEEP REINFORCEMENT LEARNING FOR EFFECTIVE CARDIAC ABLATION STRATEGY: A SIMULATION STUDY

Heart Rhythm ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. S198
Author(s):  
Hiroshi Seno ◽  
Naoki Tomii ◽  
Masatoshi Yamazaki ◽  
Nitaro Shibata ◽  
Ichiro Sakuma
Keyword(s):  
Reinforcement Learning ◽  
Simulation Study ◽  
In Silico ◽  
Cardiac Ablation

scholarly journals Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning

2018 ◽  
Vol 3 (3) ◽  
pp. 496-508 ◽  
Author(s):  
Haichen Li ◽  
Christopher R. Collins ◽  
Thomas G. Ribelli ◽  
Krzysztof Matyjaszewski ◽  
Geoffrey J. Gordon ◽  
...  
Keyword(s):  
Molecular Weight ◽  
Reinforcement Learning ◽  
Atom Transfer Radical Polymerization ◽  
Radical Polymerization ◽  
Weight Distribution ◽  
In Silico ◽  
Atom Transfer ◽  
Polymer Molecular Weight ◽  
Molecular Weight Distributions ◽  
Weight Distributions

Combination of deep reinforcement learning and atom transfer radical polymerization gives precise in silico control on polymer molecular weight distributions.

scholarly journals Controlling Blood Glucose For Patients With Type 1 DiabetesUsing Deep Reinforcement Learning – The Influence OfChanging The Reward Function

2020 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Miguel Angel Tejedor Hernandez ◽  
Jonas Nordhaug Myhre
Keyword(s):  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Blood Glucose ◽  
In Silico ◽  
Critical Component ◽  
Promising Direction ◽  
Reward Function ◽  
Reward Functions

Reinforcement learning (RL) is a promising direction in adaptive and personalized type 1 diabetes (T1D) treatment. However, the reward function – a most critical component in RL – is a component that is in most cases hand designed and often overlooked. In this paper we show that different reward functions can dramatically influence the final result when using RL to treat in-silico T1D patients.

Automatic control of cardiac ablation catheter with deep reinforcement learning method

2019 ◽  
Vol 33 (11) ◽  
pp. 5415-5423 ◽  
Author(s):  
Hyeonseok You ◽  
EunKyung Bae ◽  
Youngjin Moon ◽  
Jihoon Kweon ◽  
Jaesoon Choi
Keyword(s):  
Reinforcement Learning ◽  
Automatic Control ◽  
Learning Method ◽  
Ablation Catheter ◽  
Cardiac Ablation

scholarly journals High-power short-duration vs. standard radiofrequency cardiac ablation: comparative study based on an in-silico model

2021 ◽  
Vol 38 (1) ◽  
pp. 582-592
Author(s):  
Sergi Coderch-Navarro ◽  
Enrique Berjano ◽  
Oscar Camara ◽  
Ana González-Suárez
Keyword(s):  
Comparative Study ◽  
Short Duration ◽  
High Power ◽  
In Silico ◽  
Cardiac Ablation ◽  
In Silico Model
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