ArticleJournal of the American Medical Informatics Association : JAMIA2019
Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs).
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- MedGCN: Medication recommendation and lab test imputation via graph convolutional networks.Journal of biomedical informatics · 2022Article
- Importance-aware personalized learning for early risk prediction using static and dynamic health data.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Putting the "why" in "EHR": capturing and coding clinical cognition.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.BMC medical informatics and decision making · 2019Article
- Relation Extraction from Clinical Narratives Using Pre-trained Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA relation classification challenge datasets. Experiments show that Seg-GCRN attains state-of-the-art micro-averaged F-measure for all 3 relation categories: 0.692 for classifying medical treatment-problem relations, 0.827 for medical test-problem relations, and 0.741 for medical problem-medical problem relations. Comparison with the previous state-of-the-art segment convolutional neural network (Seg-CNN) suggests that adding syntactic dependency information helps refine medical word embedding and improves concept relation classification without manual feature engineering. Seg-GCRN can be trained efficiently for the i2b2/VA dataset on a GPU platform.
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Registered trials
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