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An AI based digital-twin for prioritising pneumonia patient treatment

Neeraj Kavan Chakshu, Perumal Nithiarasu Orcid Logo

Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, Volume: 236, Issue: 11, Pages: 1662 - 1674

Swansea University Authors: Neeraj Kavan Chakshu, Perumal Nithiarasu Orcid Logo

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DOI (Published version): 10.1177/09544119221123431

Abstract

A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strat...

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Published in: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
ISSN: 0954-4119 2041-3033
Published: SAGE Publications 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60984
first_indexed 2022-08-30T13:34:48Z
last_indexed 2023-01-13T19:21:31Z
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spelling 2022-11-20T10:43:55.9411792 v2 60984 2022-08-30 An AI based digital-twin for prioritising pneumonia patient treatment e21c85ee9062e9be0fff8ab9d77b14d7 Neeraj Kavan Chakshu Neeraj Kavan Chakshu true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2022-08-30 ACEM A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strategy is proposed to generate severity indices to: 1) identify urgent cases, 2) assign critical care and mechanical ventilation, and 3) discontinue mechanical ventilation and critical care at the optimal time. The severity indices calculated in the present study are the probability of death and the probability of requiring mechanical ventilation. These enable the generation of patient prioritisation lists and facilitates the smooth flow of patients in and out of Intensive Therapy Units (ITUs). The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. The severity indices calculated in the present study are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89. This model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions. The digital-twin model developed and tested is available via accompanyingsupplementary material. Journal Article Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236 11 1662 1674 SAGE Publications 0954-4119 2041-3033 COVID-19, pneumonia, digital-twin, artificial intelligence, ITU 1 11 2022 2022-11-01 10.1177/09544119221123431 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University The second author acknowledges partial support from Ser Cymru III - Tackling Covid 19 fund, Welsh Government Project number 095. 2022-11-20T10:43:55.9411792 2022-08-30T14:30:56.0923126 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Neeraj Kavan Chakshu 1 Perumal Nithiarasu 0000-0002-4901-2980 2 60984__25835__1ae07973a26f44469df03c4f879c78bb.pdf 60984.pdf 2022-11-20T10:41:12.8207645 Output 2650397 application/pdf Version of Record true This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License true eng https://creativecommons.org/licenses/by-nc/4.0/
title An AI based digital-twin for prioritising pneumonia patient treatment
spellingShingle An AI based digital-twin for prioritising pneumonia patient treatment
Neeraj Kavan Chakshu
Perumal Nithiarasu
title_short An AI based digital-twin for prioritising pneumonia patient treatment
title_full An AI based digital-twin for prioritising pneumonia patient treatment
title_fullStr An AI based digital-twin for prioritising pneumonia patient treatment
title_full_unstemmed An AI based digital-twin for prioritising pneumonia patient treatment
title_sort An AI based digital-twin for prioritising pneumonia patient treatment
author_id_str_mv e21c85ee9062e9be0fff8ab9d77b14d7
3b28bf59358fc2b9bd9a46897dbfc92d
author_id_fullname_str_mv e21c85ee9062e9be0fff8ab9d77b14d7_***_Neeraj Kavan Chakshu
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author Neeraj Kavan Chakshu
Perumal Nithiarasu
author2 Neeraj Kavan Chakshu
Perumal Nithiarasu
format Journal article
container_title Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine
container_volume 236
container_issue 11
container_start_page 1662
publishDate 2022
institution Swansea University
issn 0954-4119
2041-3033
doi_str_mv 10.1177/09544119221123431
publisher SAGE Publications
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strategy is proposed to generate severity indices to: 1) identify urgent cases, 2) assign critical care and mechanical ventilation, and 3) discontinue mechanical ventilation and critical care at the optimal time. The severity indices calculated in the present study are the probability of death and the probability of requiring mechanical ventilation. These enable the generation of patient prioritisation lists and facilitates the smooth flow of patients in and out of Intensive Therapy Units (ITUs). The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. The severity indices calculated in the present study are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89. This model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions. The digital-twin model developed and tested is available via accompanyingsupplementary material.
published_date 2022-11-01T02:33:57Z
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score 11.048042

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