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Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit

Soukaina Rhazzafe Orcid Logo, Fabio Caraffini Orcid Logo, Simon Colreavy-Donnelly Orcid Logo, Younes Dhassi Orcid Logo, Stefan Kuhn Orcid Logo, Nikola S. Nikolov Orcid Logo

Applied Sciences, Volume: 14, Issue: 13, Start page: 5809

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.3390/app14135809

Abstract

: Electronic health records (EHRs) are a critical tool in healthcare and capture a wide arrayof patient information that can inform clinical decision-making. However, the sheer volume andcomplexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such...

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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66965
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spelling 2024-09-12T14:35:02.8731459 v2 66965 2024-07-04 Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2024-07-04 MACS : Electronic health records (EHRs) are a critical tool in healthcare and capture a wide arrayof patient information that can inform clinical decision-making. However, the sheer volume andcomplexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as intensive care units (ICUs). To address this problem, the automatic summarizationof the main problems of patients from daily progress notes can be extremely helpful. Furthermore, byaccurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management canbe optimized, allowing for a more efficient flow of patients within the healthcare system. This workproposes a hybrid method to summarize EHR notes and studies the potential of these summariestogether with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with aconcept-based method combined with a text-to-text transfer transformer (T5), which shows the mostpromising results. By integrating the generated summaries and diagnoses with other features, ourstudy contributes to the accurate prediction of LOSs, with a support vector machine emerging as ourbest-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlightingthe potential for optimal allocation of resources within ICUs. Journal Article Applied Sciences 14 13 5809 MDPI AG 2076-3417 natural language processing (NLP); text summarization; electronic health records (EHR);intensive care unit (ICU); length of stay (LOS); MIMIC-III; classification 3 7 2024 2024-07-03 10.3390/app14135809 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee This work was conducted with the financial support of Erasmus+ ICM, funded by the European Union, project number 2020-1-IE02-KA107-000730, and the Science Foundation Ireland Centre for Research Training in Artificial Intelligence under grant No. 18/CRT/6223. The views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them. 2024-09-12T14:35:02.8731459 2024-07-04T23:15:19.6950886 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Soukaina Rhazzafe 0009-0006-5846-4897 1 Fabio Caraffini 0000-0001-9199-7368 2 Simon Colreavy-Donnelly 0000-0002-1795-6995 3 Younes Dhassi 0000-0002-6837-1671 4 Stefan Kuhn 0000-0002-5990-4157 5 Nikola S. Nikolov 0000-0001-8022-0297 6 66965__31281__d598295d715549809e228f467ad15dea.pdf 66965.VOR.pdf 2024-09-06T16:16:04.3822669 Output 342408 application/pdf Version of Record true Copyright: © 2024 by the authors. 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 true eng https://creativecommons.org/licenses/by/4.0/
title Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
spellingShingle Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
Fabio Caraffini
title_short Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
title_full Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
title_fullStr Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
title_full_unstemmed Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
title_sort Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Soukaina Rhazzafe
Fabio Caraffini
Simon Colreavy-Donnelly
Younes Dhassi
Stefan Kuhn
Nikola S. Nikolov
format Journal article
container_title Applied Sciences
container_volume 14
container_issue 13
container_start_page 5809
publishDate 2024
institution Swansea University
issn 2076-3417
doi_str_mv 10.3390/app14135809
publisher MDPI AG
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 Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description : Electronic health records (EHRs) are a critical tool in healthcare and capture a wide arrayof patient information that can inform clinical decision-making. However, the sheer volume andcomplexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as intensive care units (ICUs). To address this problem, the automatic summarizationof the main problems of patients from daily progress notes can be extremely helpful. Furthermore, byaccurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management canbe optimized, allowing for a more efficient flow of patients within the healthcare system. This workproposes a hybrid method to summarize EHR notes and studies the potential of these summariestogether with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with aconcept-based method combined with a text-to-text transfer transformer (T5), which shows the mostpromising results. By integrating the generated summaries and diagnoses with other features, ourstudy contributes to the accurate prediction of LOSs, with a support vector machine emerging as ourbest-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlightingthe potential for optimal allocation of resources within ICUs.
published_date 2024-07-03T08:21:31Z
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