Imperial College London
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Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Peng:2018,
author = {Peng, X and Ding, Y and Wihl, D and Gottesman, O and Komorowski, M and Lehman, L-WH and Ross, A and Faisal, A and Doshi-Velez, F},
pages = {887--896},
title = {Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.},
url = {https://www.ncbi.nlm.nih.gov/pubmed/30815131},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
AU  - Peng,X
AU  - Ding,Y
AU  - Wihl,D
AU  - Gottesman,O
AU  - Komorowski,M
AU  - Lehman,L-WH
AU  - Ross,A
AU  - Faisal,A
AU  - Doshi-Velez,F
EP  - 896
PY  - 2018///
SP  - 887
TI  - Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30815131
ER  -
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