The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Early Detection Assistance System for Rare Diseases based on Patient's Symptom Information

환자 증상정보 기반 희귀질환 조기 발견 보조시스템

  • Received : 2023.02.16
  • Accepted : 2023.04.17
  • Published : 2023.04.30
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Abstract

Untypical symptoms and lack of diagnostic records make it difficult for even medical specialists to detect rare diseases. Thus, it takes a lot of time and money from the onset of symptoms to an accurate diagnosis, which seriously results in physical, mental, and economic pressure on patients. In this paper, we propose and implement an early detection assistance system for rare diseases using web crawling and text mining, which can suggest the names of suspected rare diseases so that medical staffs can easily recall the disease names and make a final diagnosis of the rare diseases.

희귀질환은 증상이 전형적이지 않고 진단정보가 부족하여 전문의들조차 증상을 기반으로 질환을 의심하거나 질환명을 떠올리는 데에 어려움을 겪는다. 따라서 증상이 시작한 시점에서부터 정확한 진단을 받기까지 많은 시간 및 비용이 발생하며, 이는 환자의 신체적, 정신적, 경제적 부담을 심각하게 초래한다. 환자의 증상정보를 통해 의심되는 희귀질환을 제시하여 의사의 진단에 활용할 수 있도록, 본 논문에서는 웹 크롤링 및 텍스트마이닝을 활용한 희귀질환 조기 발견 보조시스템을 제안하고 이를 구현한다.

Keywords

Acknowledgement

본 연구는 2020년 동서대학교 학술연구조성비 지원에 의하여 이루어진 것임. (DSU-20200023)

References

  1. E. Choi and J. Lee, "Key findings from 2020 annual report on rare disease patients in Korea: incedince, mortality and medical service utilization," Annual Report, 2022.
  2. E. Choi, "Investigation of unmet needs for making diverse policy measures rare diseases," Report, 2021.
  3. Y. Na, N. Jang, and J. Won, "Statistical analysis of domestic laboratory accidents using classification criteria of KCD 7 and OIICS," Journal of the Korean Society of Safety, vol. 34, no. 3, 2019, pp. 42-49.
  4. K. Kang and S. Park, "Keyword analysis of KCI journals on business administration using web crawling and machine learning," Korean Journal of Business Administration, vol. 32, no. 4, 2019, pp. 597-615. https://doi.org/10.18032/kaaba.2019.32.4.597
  5. S. Kim and D. Cho, "Design and implementation of hashtag recommendation system based on image label extraction using deep learning," The Journal of the Korea Institute of Electronic Communication Sciences, vol. 15, no. 4, 2020, pp. 709-716.
  6. Y. Jung and Y. Ju, "Analysis of text mining of consumer's personality implication words in review of used transaction application ," The Journal of the Korea Contents Association, vol. 21, no. 11, 2021, pp. 1-10.
  7. Y. Park, S. Hwang, S. Lee, M. Kim, and S. Kim, "Text mining-based child sentiment analysis using child sentiment dictionary," In Proceedings of Korean Institute of Communications and Information Sciences Conference, Jeju, South Korea, 2022, pp. 1602-1603.
  8. J. Choi, D. Kim, M. Kang, and S. Kim, "Early detection assistance system for rare diseases using web crawling and text mining," In Proceedings of Korean Institute of Communications and Information Sciences Conference, Jeju, South Korea, 2022, pp. 1605.
  9. E. Park and S. Cho, "KoNLPy: Korean natural language processing in python," In Proceedings of the 26th Annual Conference on Human and Cognitive Language Technology, Chuncheon, South Korea, 2014, pp. 133-136.
  10. J. Lee, H. Han, K. Cho, and H. Lee, "Characteristics analysis of news articles using KoNLPy," In Proceedings of Korean Institute of Information Technology (KIIT) Conference, Cheongju, South Korea, 2020, pp. 628-629.
  11. B. Choe, I. Lee, and S. Lee, "Korean morphological analyzer for neologism and spacing error based on sequence-to-sequence," The Journal of the Korea Institute of Information Scientists and Engineers, vol. 47, no. 1, 2020, pp. 70-77. https://doi.org/10.5626/JOK.2020.47.1.70
  12. Y. Park, "A study on lexical amiguity resolution of Korean morphological analyzer," The Journal of the Korea Institute of Electronic Communication Sciences, vol. 7, no. 4, 2012, pp. 783-787.