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Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
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Fabio Caraffini ,
Simon Colreavy-Donnelly ,
Younes Dhassi ,
Stefan Kuhn ,
Nikola S. Nikolov
Applied Sciences, Volume: 14, Issue: 13, Start page: 5809
Swansea University Author:
Fabio Caraffini
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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...
| Published in: | Applied Sciences |
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| ISSN: | 2076-3417 |
| Published: |
MDPI AG
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66965 |
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| 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 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. |
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| Keywords: |
natural language processing (NLP); text summarization; electronic health records (EHR);intensive care unit (ICU); length of stay (LOS); MIMIC-III; classification |
| College: |
Faculty of Science and Engineering |
| Funders: |
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. |
| Issue: |
13 |
| Start Page: |
5809 |