Topic Modeling for International Patients' Consultations Using Natural Language Processing.

Journal: Studies in health technology and informatics
Published Date: May 25, 2022

Abstract

We extracted major topic by applying natural language processing and keyword extracting using TF, TF-IDF, TextRank, Yake, KeyBERT. 1452 consultation data were collected from the website and official hospital e-mail. We found six topics categorized into "Medical opinion" related to hospital characteristics and "Non-medical service guidance". Based on this result, it is necessary to establish marketing plan and develop a digital solution for effective consultation.

Authors

  • Sunmi Lee
    Wadsworth Center, Environmental Health Sciences, New York State Department of Health, Albany, New York.
  • Sookkyung Sung
    International Healthcare Center, Asan medical center, Seoul, Republic of Korea.
  • Seo Young Kang
    International Healthcare Center, Asan medical center, Seoul, Republic of Korea.
  • Sujin Ha
    International Healthcare Center, Asan medical center, Seoul, Republic of Korea.
  • Eunjoo Jeon
    College of Nursing, Seoul National University.

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