Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse
Open accessAbstract
Objective: The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW). Materials and Methods: We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW. Results: We identified patients with “Lupus and diarrhea,” “Crohn’s and diabetes,” and “NPHP1” from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively. Conclusion: The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.
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FH and negation detection are listed as important attributes in clinical information extraction.
“FH and negation detection are listed as important attributes in clinical information extraction [4].”
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 [4]
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Additional file 1 of The prediction of hospital length of stay using unstructured data
Chrusciel, J., Girardon, F., Roquette, L., Laplanche, D., Duclos, A., & Sanchez, S. (2021) · Figshare · doi:10.6084/m9.figshare.17284340
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Additional file 2 of The prediction of hospital length of stay using unstructured data
Chrusciel, J., Girardon, F., Roquette, L., Laplanche, D., Duclos, A., & Sanchez, S. (2021) · Figshare · doi:10.6084/m9.figshare.17284343
Digan, W. E. (2020) · theses.fr (ABES) · doi:10.70675/c1537cecz6efdz4a4bzb8c5z768ab8f06491
- Authors
- Garcelon, N., Neuraz, A., Benoit, V., Salomon, R., & Burgun, A.
- Affiliations
- Inserm (FR)
- Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France
- Hôpital Necker-Enfants Malades (FR)
- Corresponding author
- Garcelon, Nicolas
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-10-20
- Volume
- 24
- Issue
- 3
- Pages
- 607-613
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7651926
- OpenAlex
- W2532802663
- Cited by
- 48
- References
- 29
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 14 · 2020: 6 · 2019: 5 · 2018: 7 · 2017: 4
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- negation detection, full-text search engine, rare diseases, clinical data warehouse, electronic health records, case finding, NPHP1, Crohn's disease, lupus
- MeSH terms
- Data Warehousing, Family Health, Humans, Medical History Taking, Natural Language Processing, Information Storage and Retrieval, Rare Diseases, Electronic Health Records, Search Engine
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