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Deep Learning Meets Biomedical Ontologies: Knowledge Embeddings for Epilepsy.

Ramon Maldonado1, Travis R Goodwin1, Michael A Skinner1,2

  • 1The University of Texas at Dallas, Richardson, TX.

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|June 2, 2018
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Summary
This summary is machine-generated.

Deep learning creates Medical Knowledge Embeddings (MKE) from epilepsy data, uncovering new medical concepts and relations not found in traditional ontologies.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Traditional biomedical ontologies use a top-down approach for knowledge representation.
  • Deep learning offers a bottom-up methodology to extract knowledge from data.

Purpose of the Study:

  • To present Medical Knowledge Embeddings (MKE), a novel knowledge representation generated by deep learning.
  • To encode epilepsy-related medical concepts and their relationships from clinical text.

Main Methods:

  • Utilized deep learning methods to process vast collections of epilepsy-related medical records.
  • Developed a bottom-up approach to identify and represent medical concepts and relations.

Main Results:

  • MKE includes epilepsy concepts and relations not present in existing biomedical ontologies.
  • Demonstrated high accuracy in identifying medical concepts from clinical text.
  • Showcased promising results for the correctness and completeness of extracted relations.

Conclusions:

  • Deep learning-based MKE effectively captures complex medical knowledge from unstructured text.
  • MKE expands existing biomedical knowledge bases by incorporating data-driven insights.