Disease Spectrum Analysis and Population Health Trend Research Based on Text Data Analysis of Large-Scale Online Medical Consultation

This study investigates common health problems and online healthcare seeking behavioral patterns of individuals using a large-scale online consultation dataset extracted from well-known healthcare platforms. By using text mining techniques, including tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and topic modeling, we analyzed the textual content of these consultations to identify key themes and patterns. Our findings suggest that there are significant gender differences in online medical counseling, with women more likely to seek medical advice online than men. Additionally, the analysis identified age-related patterns of health concerns, with younger people primarily reporting minor illnesses, while older people expressed concerns about chronic conditions. These results highlight the importance of understanding the unique healthcare needs of different populations and the potential role of online platforms in meeting those needs. This study provides valuable insights into the common health concerns and patterns of online healthcare seeking behavior among individuals.