Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models.
In: Journal of Biomedical Informatics, Jg. 141 (2023-05-01), S. N.PAG
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Zugriff:
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries , written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets. [Display omitted] • Discharge summary summarisation via clinically guided deep learning. • Deep learning natural language processing models with clinical concept guidance. • Inpatient discharge summary text summarisation with natural language processing. • Ensemble extractive/abstractive text summarisation for discharge summary text. [ABSTRACT FROM AUTHOR]
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Titel: |
Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models.
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Autor/in / Beteiligte Person: | Searle, Thomas ; Ibrahim, Zina ; Teo, James ; Dobson, Richard J.B. |
Zeitschrift: | Journal of Biomedical Informatics, Jg. 141 (2023-05-01), S. N.PAG |
Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
ISSN: | 1532-0464 (print) |
DOI: | 10.1016/j.jbi.2023.104358 |
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