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Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome
Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous resear...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297554/ https://www.ncbi.nlm.nih.gov/pubmed/37370993 http://dx.doi.org/10.3390/diagnostics13122098 |
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author | Barakat, Chadi S. Sharafutdinov, Konstantin Busch, Josefine Saffaran, Sina Bates, Declan G. Hardman, Jonathan G. Schuppert, Andreas Brynjólfsson, Sigurður Fritsch, Sebastian Riedel, Morris |
author_facet | Barakat, Chadi S. Sharafutdinov, Konstantin Busch, Josefine Saffaran, Sina Bates, Declan G. Hardman, Jonathan G. Schuppert, Andreas Brynjólfsson, Sigurður Fritsch, Sebastian Riedel, Morris |
author_sort | Barakat, Chadi S. |
collection | PubMed |
description | Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R(2) > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. |
format | Online Article Text |
id | pubmed-10297554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102975542023-06-28 Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome Barakat, Chadi S. Sharafutdinov, Konstantin Busch, Josefine Saffaran, Sina Bates, Declan G. Hardman, Jonathan G. Schuppert, Andreas Brynjólfsson, Sigurður Fritsch, Sebastian Riedel, Morris Diagnostics (Basel) Article Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the “Berlin Definition”. This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R(2) > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. MDPI 2023-06-17 /pmc/articles/PMC10297554/ /pubmed/37370993 http://dx.doi.org/10.3390/diagnostics13122098 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barakat, Chadi S. Sharafutdinov, Konstantin Busch, Josefine Saffaran, Sina Bates, Declan G. Hardman, Jonathan G. Schuppert, Andreas Brynjólfsson, Sigurður Fritsch, Sebastian Riedel, Morris Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title | Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title_full | Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title_fullStr | Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title_full_unstemmed | Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title_short | Developing an Artificial Intelligence-Based Representation of a Virtual Patient Model for Real-Time Diagnosis of Acute Respiratory Distress Syndrome |
title_sort | developing an artificial intelligence-based representation of a virtual patient model for real-time diagnosis of acute respiratory distress syndrome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297554/ https://www.ncbi.nlm.nih.gov/pubmed/37370993 http://dx.doi.org/10.3390/diagnostics13122098 |
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