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An AI based digital-twin for prioritising pneumonia patient treatment
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
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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...
Published in: | Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine |
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ISSN: | 0954-4119 2041-3033 |
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SAGE Publications
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa61422 |
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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 |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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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 accompanying Supplemental material. |
published_date |
2022-09-18T04:20:14Z |
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1763754340405739520 |
score |
11.028048 |