Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.

Journal: Journal of biomedical informatics
Published Date: Nov 1, 2020

Abstract

OBJECTIVE: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text.

Authors

  • Steven Jiang
    Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
  • Weiyi Wu
    Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
  • Naofumi Tomita
    Biomedical Data Science Department, Dartmouth College, Hanover, NH 03755, USA.
  • Craig Ganoe
    Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.
  • Saeed Hassanpour
    Lia Harrington, Todd MacKenzie, and Saeed Hassanpour, Geisel School of Medicine at Dartmouth College, Hanover; Roberta diFlorio-Alexander, Katherine Trinh, and Arief Suriawinata, Dartmouth-Hitchcock Medical Center, Lebanon, NH.

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