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Detecting false-positive disease references in veterinary clinical notes without manual annotations
Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550178/ https://www.ncbi.nlm.nih.gov/pubmed/31304379 http://dx.doi.org/10.1038/s41746-019-0108-y |
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author | Kennedy, Noel Brodbelt, Dave C. Church, David B. O’Neill, Dan G. |
author_facet | Kennedy, Noel Brodbelt, Dave C. Church, David B. O’Neill, Dan G. |
author_sort | Kennedy, Noel |
collection | PubMed |
description | Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use their clinical notes to speculate about disease existence (differential diagnosis) or to state that the disease has been ruled out. To train classifiers for disambiguating disease references, previous researchers built training sets by manually annotating sentences. We show how to create very large training sets without the need for manual annotation. We obtain state-of- the-art classification performance with a bidirectional long short-term memory model trained to distinguish disease references between patients with or without the disease diagnosis in veterinary clinical notes. |
format | Online Article Text |
id | pubmed-6550178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65501782019-07-12 Detecting false-positive disease references in veterinary clinical notes without manual annotations Kennedy, Noel Brodbelt, Dave C. Church, David B. O’Neill, Dan G. NPJ Digit Med Article Clinicians often include references to diseases in clinical notes, which have not been diagnosed in their patients. For some diseases terms, the majority of disease references written in the patient notes may not refer to true disease diagnosis. These references occur because clinicians often use their clinical notes to speculate about disease existence (differential diagnosis) or to state that the disease has been ruled out. To train classifiers for disambiguating disease references, previous researchers built training sets by manually annotating sentences. We show how to create very large training sets without the need for manual annotation. We obtain state-of- the-art classification performance with a bidirectional long short-term memory model trained to distinguish disease references between patients with or without the disease diagnosis in veterinary clinical notes. Nature Publishing Group UK 2019-05-03 /pmc/articles/PMC6550178/ /pubmed/31304379 http://dx.doi.org/10.1038/s41746-019-0108-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kennedy, Noel Brodbelt, Dave C. Church, David B. O’Neill, Dan G. Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title | Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title_full | Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title_fullStr | Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title_full_unstemmed | Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title_short | Detecting false-positive disease references in veterinary clinical notes without manual annotations |
title_sort | detecting false-positive disease references in veterinary clinical notes without manual annotations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550178/ https://www.ncbi.nlm.nih.gov/pubmed/31304379 http://dx.doi.org/10.1038/s41746-019-0108-y |
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