Identifying Precautionary Measures for High-Risk Disease from Doctor's Answer Text Using LDA

The Online Health Consultation (OHC), which contains a QA collection of various diseases since 2014, has received an increasing number of visits due to the COVID-19. Based on the benefits and increasing health information need for people who seek information in OHC, health information related to precautionary measures to avoid diseases, especially high-risk diseases, become critical because not all seeker and readers of health information are diagnosed with certain diseases. However, It has currently unidentified whether the text of the doctor's answer corpus, especially in high-risk diseases, contains words that imply precautionary. This study aims to find the pattern of doctor's answer for high-risk diseases through the corpus of doctor's answer text on OHC by identifying whether the doctor's answer text contains words that imply precautionary against disease. Thus, it can help health information seekers and readers take precautionary against disease early on. This paper's contribution was to identify precautionary measures from doctor's answer text for high-risk disease in 2014-2021 using the best model of the two models, namely Single LDA (only LDA Method) and Hybrid LDA (a combination of LDA and Collapsed Gibbs Sampling). The results showed that the best model was Hybrid LDA, and medical experts identified groups of words with this model into four domains, namely symptoms/diagnosis, treatments, precautionary measurements, and general text. The pattern that emerges from the identification of precautionary measures shows (1) which precautionary measures are divided based on what disease, (2) Some words that mean precautionary measures also mean treatment or symptom/diagnosis.