@inproceedings{viani-etal-2018-time,
title = "Time Expressions in Mental Health Records for Symptom Onset Extraction",
author = "Viani, Natalia and
Yin, Lucia and
Kam, Joyce and
Alawi, Ayunni and
Bittar, Andr{\'e} and
Dutta, Rina and
Patel, Rashmi and
Stewart, Robert and
Velupillai, Sumithra",
editor = "Lavelli, Alberto and
Minard, Anne-Lyse and
Rinaldi, Fabio",
booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5621",
doi = "10.18653/v1/W18-5621",
pages = "183--192",
abstract = "For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.",
}
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<abstract>For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.</abstract>
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%0 Conference Proceedings
%T Time Expressions in Mental Health Records for Symptom Onset Extraction
%A Viani, Natalia
%A Yin, Lucia
%A Kam, Joyce
%A Alawi, Ayunni
%A Bittar, André
%A Dutta, Rina
%A Patel, Rashmi
%A Stewart, Robert
%A Velupillai, Sumithra
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Rinaldi, Fabio
%S Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F viani-etal-2018-time
%X For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.
%R 10.18653/v1/W18-5621
%U https://aclanthology.org/W18-5621
%U https://doi.org/10.18653/v1/W18-5621
%P 183-192
Markdown (Informal)
[Time Expressions in Mental Health Records for Symptom Onset Extraction](https://aclanthology.org/W18-5621) (Viani et al., Louhi 2018)
ACL
- Natalia Viani, Lucia Yin, Joyce Kam, Ayunni Alawi, André Bittar, Rina Dutta, Rashmi Patel, Robert Stewart, and Sumithra Velupillai. 2018. Time Expressions in Mental Health Records for Symptom Onset Extraction. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 183–192, Brussels, Belgium. Association for Computational Linguistics.