ArticleJMIR medical informatics2021
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.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- A Sentence Classification-Based Medical Status Extraction Pipeline for Electronic Health Records: Institutional Case Study.JMIR medical informatics · 2026Article
- Span-based annotation framework for LLM-based clinical named entity recognition: development and validation using Korean emergency department notes.JAMIA open · 2025Article
- Genomic formation of Tibeto-Burman speaking populations in Guizhou, Southwest China.BMC genomics · 2023Article
- Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis.JMIR medical informatics · 2023Article
- A scoping review of publicly available language tasks in clinical natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models.Cluster computing · 2022Article
- Identifying Patients Who Meet Criteria for Genetic Testing of Hereditary Cancers Based on Structured and Unstructured Family Health History Data in the Electronic Health Record: Natural Language Processing Approach.JMIR medical informatics · 2022Article
- Health Natural Language Processing: Methodology Development and Applications.JMIR medical informatics · 2021Article
- Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis.JMIR medical informatics · 2021Article
- A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.JMIR medical informatics · 2021Article
- Comparison of a Focused Family Cancer History Questionnaire to Family History Documentation in the Electronic Medical Record.Journal of primary care & community healthArticle
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Abstract
backgroundAs a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized.
objectiveThe n2c2/OHNLP (National NLP Clinical Challenges/Open Health Natural Language Processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives.
methodsWe organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and clinical observations with family members to be extracted. Subtask 2 is an end-to-end task which is based on the result of subtask 1. We manually curated the first deidentified clinical narrative from FH sections of clinical notes at Mayo Clinic Rochester, the content of which is highly relevant to patients' FH.
resultsA total of 17 teams from all over the world participated in the n2c2/OHNLP FH extraction shared task, where 38 runs were submitted for subtask 1 and 21 runs were submitted for subtask 2. For subtask 1, the top 3 runs were generated by Harbin Institute of Technology, ezDI, Inc., and The Medical University of South Carolina with F1 scores of 0.8745, 0.8225, and 0.8130, respectively. For subtask 2, the top 3 runs were from Harbin Institute of Technology, ezDI, Inc., and University of Florida with F1 scores of 0.681, 0.6586, and 0.6544, respectively. The workshop was held in conjunction with the AMIA 2019 Fall Symposium.
conclusionsA wide variety of methods were used by different teams in both tasks, such as Bidirectional Encoder Representations from Transformers, convolutional neural network, bidirectional long short-term memory, conditional random field, support vector machine, and rule-based strategies. System performances show that relation extraction from FH is a more challenging task when compared to entity identification task.
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