TY - JOUR
T1 - Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
AU - Rhazzafe, Soukaina
AU - Caraffini, Fabio
AU - Colreavy-Donnelly, Simon
AU - Dhassi, Younes
AU - Kuhn, Stefan
AU - Nikolov, Nikola S.
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/7
Y1 - 2024/7
N2 - Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity 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 summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs.
AB - Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity 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 summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs.
KW - MIMIC-III
KW - classification
KW - electronic health records (EHR)
KW - intensive care unit (ICU)
KW - length of stay (LOS)
KW - natural language processing (NLP)
KW - text summarization
UR - http://www.scopus.com/inward/record.url?scp=85198456267&partnerID=8YFLogxK
U2 - 10.3390/app14135809
DO - 10.3390/app14135809
M3 - Article
AN - SCOPUS:85198456267
SN - 2076-3417
VL - 14
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 13
M1 - 5809
ER -