Kormilitzin, A., Vaci, N. orcid.org/0000-0002-8094-0902, Liu, Q. et al. (3 more authors) (2020) An efficient representation of chronological events in medical texts. In: Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. OUHI 2020: The 11th International Workshop on Health Text Mining and Information Analysis, 20 Nov 2020, Online. Association for Computational Linguistics , pp. 97-103. ISBN 9781952148811
Abstract
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs). Clinical notes, which is a particular type of EHR data, are a rich source of information and practitioners often develop clever solutions how to maximise the sequential information contained in free-texts. We proposed a systematic methodology for learning from chronological events available in clinical notes. The proposed methodological path signature framework creates a non-parametric hierarchical representation of sequential events of any type and can be used as features for downstream statistical learning tasks. The methodology was developed and externally validated using the largest in the UK secondary care mental health EHR data on a specific task of predicting survival risk of patients diagnosed with Alzheimer’s disease. The signature-based model was compared to a common survival random forest model. Our results showed a 15.4% increase of risk prediction AUC at the time point of 20 months after the first admission to a specialist memory clinic and the signature method outperformed the baseline mixed-effects model by 13.2 %.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Association for Computational Linguistics. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2020 13:54 |
Last Modified: | 16 Nov 2020 13:54 |
Published Version: | https://www.aclweb.org/anthology/2020.louhi-1.11 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Identification Number: | 10.18653/v1/P17 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168039 |