잠복결핵에 관한 온라인 건강 상담 현황: 텍스트 마이닝과 머신 러닝을 활용한 분석연구

초록

The study’s objective is to explore the health information needs of latent tuberculosis patients and theircommunities by analyzing data from the online health counselling platform, Naver Jisik-iN. Initially, 3,261 questionsrelated to ‘latent tuberculosis’ were collected. Following the removal of duplicates and irrelevant image information, thefinal dataset for analysis comprised 2,198 questions. Text pre-processing, Latent Dirichlet Allocation (LDA) topicmodelling, and Long Short-Term Memory (LSTM)-based text summarization model were used. Manual categorization wasadded to supplement the unsupervised learning process. Seven topics were identified using LDA, from which five specifictopics (‘side effects’, ‘treatment’, ‘army’, ‘interaction’, and ‘infectiousness’) were derived. Subsequently, manual classificationwas conducted based on these five topics. Manual summary and LSTM-based text summarization results were consistent. Numerous individuals sought information about the potential for curing latent tuberculosis and the risk of tuberculosisdevelopment. Moreover, questions related to the interpretation of test results and interactions with other substances werewidespread. Concerning side effects, issues predominantly revolved around drug discontinuation due to skin problems andelevated liver function tests. The findings reveal the prevalent concerns and inquiries of society regarding latenttuberculosis. The identified topics offer valuable insights into the key aspects of interest related to this condition.

키워드