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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doaj-f5a32a8957e043d9834a6be68505b20b2021-01-17T12:34:23ZengNature Publishing GroupScientific Reports2045-23222021-01-0111111210.1038/s41598-020-80457-0A natural language processing approach for identifying temporal disease onset information from mental healthcare textNatalia Viani0Riley Botelle1Jack Kerwin2Lucia Yin3Rashmi Patel4Robert Stewart5Sumithra Velupillai6IoPPN, King’s College LondonIoPPN, King’s College LondonIoPPN, King’s College LondonIoPPN, King’s College LondonIoPPN, King’s College LondonIoPPN, King’s College LondonIoPPN, King’s College LondonAbstract 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.https://doi.org/10.1038/s41598-020-80457-0 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Natalia Viani Riley Botelle Jack Kerwin Lucia Yin Rashmi Patel Robert Stewart Sumithra Velupillai |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-01-01 |
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. |
url |
https://doi.org/10.1038/s41598-020-80457-0 |
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