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Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning
We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine l...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632869/ https://www.ncbi.nlm.nih.gov/pubmed/36327195 http://dx.doi.org/10.1371/journal.pone.0275358 |
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author | Böck, Markus Malle, Julien Pasterk, Daniel Kukina, Hrvoje Hasani, Ramin Heitzinger, Clemens |
author_facet | Böck, Markus Malle, Julien Pasterk, Daniel Kukina, Hrvoje Hasani, Ramin Heitzinger, Clemens |
author_sort | Böck, Markus |
collection | PubMed |
description | We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic. |
format | Online Article Text |
id | pubmed-9632869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96328692022-11-04 Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning Böck, Markus Malle, Julien Pasterk, Daniel Kukina, Hrvoje Hasani, Ramin Heitzinger, Clemens PLoS One Research Article We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic. Public Library of Science 2022-11-03 /pmc/articles/PMC9632869/ /pubmed/36327195 http://dx.doi.org/10.1371/journal.pone.0275358 Text en © 2022 Böck et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Böck, Markus Malle, Julien Pasterk, Daniel Kukina, Hrvoje Hasani, Ramin Heitzinger, Clemens Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title | Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title_full | Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title_fullStr | Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title_full_unstemmed | Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title_short | Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning |
title_sort | superhuman performance on sepsis mimic-iii data by distributional reinforcement learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632869/ https://www.ncbi.nlm.nih.gov/pubmed/36327195 http://dx.doi.org/10.1371/journal.pone.0275358 |
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