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        <title>An efficient representation of chronological events in medical texts</title>
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    <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 %.</abstract>
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%0 Conference Proceedings
%T An efficient representation of chronological events in medical texts
%A Kormilitzin, Andrey
%A Vaci, Nemanja
%A Liu, Qiang
%A Ni, Hao
%A Nenadic, Goran
%A Nevado-Holgado, Alejo
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kormilitzin-etal-2020-efficient
%X 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 %.
%R 10.18653/v1/2020.louhi-1.11
%U https://aclanthology.org/2020.louhi-1.11
%U https://doi.org/10.18653/v1/2020.louhi-1.11
%P 97-103

Markdown (Informal)

[An efficient representation of chronological events in medical texts](https://aclanthology.org/2020.louhi-1.11) (Kormilitzin et al., Louhi 2020)

• An efficient representation of chronological events in medical texts (Kormilitzin et al., Louhi 2020)

ACL

• Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic, and Alejo Nevado-Holgado. 2020. An efficient representation of chronological events in medical texts. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 97–103, Online. Association for Computational Linguistics.