ArticleJournal of the American Medical Informatics Association : JAMIA2018
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
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- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.JMIR medical informatics · 2021Article
- [Research on entity relationship extraction of Chinese medical literature and application in diabetes medical literature].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.Journal of medical Internet research · 2021Article
- Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study.JMIR medical informatics · 2021Article
- Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction.Journal of biomedical informatics · 2021Article
- Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.BMC medical informatics and decision making · 2020Article
- Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.Neuroradiology · 2020Article
- The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Using UMLS for electronic health data standardization and database design.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Extracting entities with attributes in clinical text via joint deep learning.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications.Yearbook of medical informatics · 2019Review
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Distant supervision for treatment relation extraction by leveraging MeSH subheadings.Artificial intelligence in medicine · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.