Family history information extraction via deep joint learning
Open accessAbstract
Background: Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.
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During the 2018 BioCreative/OHNLP Challenge, Shi et al explored a joint deep learning approach and achieved the best performance among all participated teams.
“During that challenge, Shi et al [35] explored a joint deep learning approach and achieved the best performance among all participated teams.”
From Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models (2020) · cites this paper as [35]
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References 16 · Cited by 15
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- Authors
- Shi, X., Jiang, D., Huang, Y., Wang, X., Chen, Q., Yan, J., & Tang, B.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Tang, Buzhou
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2019-12
- Volume
- 19
- Issue
- S10
- Pages
- 277-277
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6933634
- OpenAlex
- W2996891673
- Cited by
- 15
- References
- 16
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 3 · 2021: 5 · 2020: 5
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Harbin Institute of Technology (HIT.NSRIF.2017052)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- clinical text, electronic health records, entity identification
- MeSH terms
- Clinical Decision-Making, Deep Learning, Algorithms, Humans, Medical History Taking, Natural Language Processing, Information Storage and Retrieval, Computational Biology, Electronic Health Records
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