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An AI based digital-twin for prioritising pneumonia patient treatment

Neeraj Kavan Chakshu Orcid Logo, Perumal Nithiarasu Orcid Logo

Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, Volume: 236, Issue: 11, Start page: 095441192211234

Swansea University Author: 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/cronfa61422
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first_indexed 2022-10-25T16:17:39Z
last_indexed 2023-01-13T19:22:10Z
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spelling 2023-01-05T15:35:31.6398903 v2 61422 2022-10-05 An AI based digital-twin for prioritising pneumonia patient treatment 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2022-10-05 CIVL 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 accompanying Supplemental material. Journal Article Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236 11 095441192211234 SAGE Publications 0954-4119 2041-3033 COVID-19, pneumonia, digital-twin, artificial intelligence, ITU 18 9 2022 2022-09-18 10.1177/09544119221123431 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University The second author acknowledges partial support from Ser Cymru III - Tackling Covid 19 fund, Welsh Government Project number 095. 2023-01-05T15:35:31.6398903 2022-10-05T08:37:31.7634557 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Neeraj Kavan Chakshu 0000-0002-6430-7939 1 Perumal Nithiarasu 0000-0002-4901-2980 2 61422__25584__b8184e653c424aa59c3a805c509ea225.pdf 61422_VoR.pdf 2022-10-25T17:18:14.2133467 Output 2650323 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
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 3b28bf59358fc2b9bd9a46897dbfc92d
author_id_fullname_str_mv 3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author 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 095441192211234
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
hierarchytype
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
document_store_str 1
active_str 0
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 accompanying Supplemental material.
published_date 2022-09-18T04:20:14Z
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score 11.017797

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