Dependency Parsing for Extracting Family History
Abstract
Family history is an important part of the clinical record. Automatically extracting family history from clinical reports requires an understanding of how clinicians describe family history as well as an understanding of how they discuss diseases. We perform a characteristic analysis of family history sentences and compare them with sentences which contain a family member term but no family history. We observe the frequency of family history in clinical reports, the reliability of using family member mentions to indicate family history, as well as the frequency that family history is labeled. We develop a top down processing syntactic approach to extracting family history, perform an analysis on the distribution of syntactic context in family history, and compare the use of a phrase structure parser versus a dependency parser for extraction.
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The progress of applying NLP methods and resources to biomedical texts, including FH extraction, has been limited by difficulties in accessing shared tools and resources, partially caused by patient privacy and data confidentiality constraints.
“Although the application of NLP methods and resources to biomedical texts has received increasing attention [6-8], with methods for FH extraction [9-11], the progress has been limited by difficulties in accessing shared tools and resources, partially caused by patient privacy and data confidentiality constraints.”
From Family History Extraction From Synthetic Clinical Narratives Using Natural Language Processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health Natural Language Processing (OHNLP) Competition (2021) · cites this paper as [10]
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References 14 · Cited by 7
- Authors
- Lewis, N., Gruhl, D., & Yang, H.
- Affiliations
- IBM (United States) (US)
- San Francisco State University (US)
- Venue
- 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology
- Publisher
- IEEE
- Published
- 2011-07
- Pages
- 237-242
- OpenAlex
- W2125033540
- Cited by
- 7
- References
- 14
- Citations by year
- 2020: 1 · 2015: 3 · 2014: 2 · 2012: 1
- FWCI
- 3.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- dependency parsing, clinical reports, phrase structure parsing, top-down parsing, syntactic context, clinical natural language processing
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