Enrich rare disease phenotypic characterizations via a graph convolutional network based recommendation system

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, there exist more than 300 million patients affected by about 7,000 rare disease all over the world, which comprises 3.5% to 5.9% of the global population. 40% of rare disease patients are diagnosed incorrectly before reaching a final diagnosis, of which 25% spend between 5 to 30 years on a chaotic journey through numerous referrals, investigations, and disease evolutions from early symptoms to a confirmatory diagnosis of their disease. Phenotypes are defined as observable characteristics and clinical traits of diseases and organisms. A significant lack of knowledge and insufficient characterization of the longitudinal phenotypic information of many rare diseases is a significant contributor to the continued existence of such diagnostic odyssey. In this study, to largely detect longitudinal phenotypic characterizations for rare disease, we formulated the problem of enriching rare disease phenotypic sets as a phenotype recommendation task and applied the graph convolutional network along with biomedical knowledge graph over Mayo Clinic electronic health records to achieve the goal.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
EditorsAlba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-40
Number of pages4
ISBN (Electronic)9781728194295
DOIs
StatePublished - Jul 2020
Event33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States
Duration: Jul 28 2020Jul 30 2020

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2020-July
ISSN (Print)1063-7125

Conference

Conference33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
CountryUnited States
CityVirtual, Online
Period7/28/207/30/20

Keywords

  • Biomedical knowledge graph
  • Graph convolutional network
  • Phenotypic characterization
  • Rare disease
  • Recommendation system

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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