A natural language processing approach for identifying temporal disease onset information from mental healthcare text

Abstract Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associ...

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Autores principales:	Natalia Viani, Riley Botelle, Jack Kerwin, Lucia Yin, Rashmi Patel, Robert Stewart, Sumithra Velupillai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f5a32a8957e043d9834a6be68505b20b
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spelling	oai:doaj.org-article:f5a32a8957e043d9834a6be68505b20b2021-12-02T15:22:59ZA natural language processing approach for identifying temporal disease onset information from mental healthcare text10.1038/s41598-020-80457-02045-2322https://doaj.org/article/f5a32a8957e043d9834a6be68505b20b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80457-0https://doaj.org/toc/2045-2322Abstract Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient’s care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.Natalia VianiRiley BotelleJack KerwinLucia YinRashmi PatelRobert StewartSumithra VelupillaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Natalia Viani Riley Botelle Jack Kerwin Lucia Yin Rashmi Patel Robert Stewart Sumithra Velupillai A natural language processing approach for identifying temporal disease onset information from mental healthcare text
description	Abstract Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient’s care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.
format	article
author	Natalia Viani Riley Botelle Jack Kerwin Lucia Yin Rashmi Patel Robert Stewart Sumithra Velupillai
author_facet	Natalia Viani Riley Botelle Jack Kerwin Lucia Yin Rashmi Patel Robert Stewart Sumithra Velupillai
author_sort	Natalia Viani
title	A natural language processing approach for identifying temporal disease onset information from mental healthcare text
title_short	A natural language processing approach for identifying temporal disease onset information from mental healthcare text
title_full	A natural language processing approach for identifying temporal disease onset information from mental healthcare text
title_fullStr	A natural language processing approach for identifying temporal disease onset information from mental healthcare text
title_full_unstemmed	A natural language processing approach for identifying temporal disease onset information from mental healthcare text
title_sort	natural language processing approach for identifying temporal disease onset information from mental healthcare text
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f5a32a8957e043d9834a6be68505b20b
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A natural language processing approach for identifying temporal disease onset information from mental healthcare text

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