CSnet: Constructing symptom network based on disease-symptom relationships

Sohee Hwang, Jungrim Kim, Jeongwoo Kim, Sanghyun Park

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

3 Citations (Scopus)

Abstract

A symptom is the physical indication of an unstable state or the beginning of diseases. Symptom analysis is an essential factor in the medical area, where it is used for disease diagnosis, drug prescription, and the development of new pharmaceuticals. Commensurate with its importance, symptom analysis has been the subject of various studies in recent years. However, prior literature on this topic has been largely limited to studying symptoms for a specific disease. Our paper attempts to expand and build on previous studies by introducing a network-based symptom analysis. Symptom analysis that can provide a basis for analyzing symptoms related to various diseases. For a universal symptom analysis system, we proposed a network-based symptom analysis. In order to construct a symptom network, we utilized Medical Subject Heading (MeSH) terms and the PubMed search engine which are maintained and developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM). We identified symptomdisease relationships with two measurements, the term frequency-inverse document frequency (TF-IDF) and frequent occurrence of two terms (co-occurrence) from PubMed articles. Symptom-symptom pairs, which is the outline for symptom network, were built up based on symptom-disease relationships. As a result, we constructed a symptom network with 223 nodes and 5313 edges. Evaluations were performed in two ways, compared with two symptom clusters and demonstrated with previous researches. Additionally, proposed method has shown possibility for a guideline of clinical demonstration and a discovery of potential symptoms pair.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages960-965
Number of pages6
ISBN (Electronic)9781538616451
DOIs
Publication statusPublished - 2017 Nov 27
Event2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada
Duration: 2017 Oct 52017 Oct 8

Publication series

Name2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
CountryCanada
CityBanff
Period17/10/517/10/8

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Control and Optimization

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  • Cite this

    Hwang, S., Kim, J., Kim, J., & Park, S. (2017). CSnet: Constructing symptom network based on disease-symptom relationships. In 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 (pp. 960-965). (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2017.8122734