ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Improving medical term embeddings using UMLS Metathesaurus.BMC medical informatics and decision making · 2022Article
- Developing a RadLex-Based Named Entity Recognition Tool for Mining Textual Radiology Reports: Development and Performance Evaluation Study.Journal of medical Internet research · 2021Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Active deep learning for the identification of concepts and relations in electroencephalography reports.Journal of biomedical informatics · 2019Article
- Adversarial Learning of Knowledge Embeddings for the Unified Medical Language System.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
While biomedical ontologies have traditionally been used to guide the identification of concepts or relations in biomedical data, recent advances in deep learning are able to capture high-quality knowledge from textual data and represent it in graphical structures. As opposed to the top-down methodology used in the generation of ontologies, which starts with the principled design of the upper ontology, the bottom-up methodology enabled by deep learning encodes the likelihood that concepts share certain relations, as evidenced by data. In this paper, we present a knowledge representation produced by deep learning methods, called Medical Knowledge Embeddings (MKE), that encode medical concepts related to the study of epilepsy and the relations between them. Many of the epilepsy-relevant medical concepts from MKE are not yet available in existing biomedical ontologies, but are mentioned in vast collections of epilepsy-related medical records which also imply their relationships. The evaluation of the MKE indicates high accuracy of the medical concepts automatically identified from clinical text as well as promising results in terms of correctness and completeness of relations produced by deep learning.
Indexed as
Identifiers
29854192PMC5977726What OpenQuestion holds
Registered trials
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