Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment.
Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-02-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000012 |
| _version_ | 1797701794478948352 |
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| author | Thesath Nanayakkara Gilles Clermont Christopher James Langmead David Swigon |
| author_facet | Thesath Nanayakkara Gilles Clermont Christopher James Langmead David Swigon |
| author_sort | Thesath Nanayakkara |
| collection | DOAJ |
| description | Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research. |
| first_indexed | 2024-03-12T04:41:50Z |
| format | Article |
| id | doaj.art-4c5322ff44674ffc9d266d915db98548 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T04:41:50Z |
| publishDate | 2022-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-4c5322ff44674ffc9d266d915db985482023-09-03T09:38:23ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-02-0112e000001210.1371/journal.pdig.0000012Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment.Thesath NanayakkaraGilles ClermontChristopher James LangmeadDavid SwigonSepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research.https://doi.org/10.1371/journal.pdig.0000012 |
| spellingShingle | Thesath Nanayakkara Gilles Clermont Christopher James Langmead David Swigon Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. PLOS Digital Health |
| title | Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. |
| title_full | Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. |
| title_fullStr | Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. |
| title_full_unstemmed | Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. |
| title_short | Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment. |
| title_sort | unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment |
| url | https://doi.org/10.1371/journal.pdig.0000012 |
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