BACKGROUND
With the development of the Internet, online medical community can help patient access to medical information and relevant decisions more conveniently, and meet the needs of patients for their own healthcare management. Mining these Q&A (Question and Answer) data, we can help doctors give more targeted feedback which improve the efficiency of question-and-answer, and patient satisfaction.
OBJECTIVE
This study aimed to (1) analysis frequency and position of diabetes related diseases or symptoms in Q&A website and (2) find out the differences of disease terms in gender and age using in the questions.
METHODS
We collected 5766 Q&A diabetes related data on the website of Chunyuyisheng from June 2012 to April 2020. In 38176 combined sentences, a vocabulary contains 3 categories of 3851 word and 2094 ICD (International Classification of Diseases) matching terms were obtained by calculating the similarity using word vectors. Proportion of the frequency of words and Mann-Whitney U test on word position were used to quantify the difference in patient’s gender and age group.
RESULTS
The vocabulary of the disease category accounts for 70%. We analyzed the word frequency and position in questions for different gender and age group. For gender, women participate in question answering more, accounting for 53% of total questions. They pay more attention to pregnancy, sleep and thyroid gland related vocabulary compared to men. Men focus more on circulation system, kidney failure related vocabulary. For different age group, pregnancy, glucose regulation, digestive and respiratory system related vocabulary have a higher proportion for patients under 40 years old. Patients over 40 years old pay more attention on kidney failure, cerebral ischaemia, infectious and circulation system.
CONCLUSIONS
This study provides a new insight into frequency and position of diabetes related diseases or symptoms in online medical services. It can show patients’ different attention by comparing disease or symptom categories for gender and age with ICD disease codes. The frequency and position of disease category words in patients’ conversation can be used for further risk evaluation for chronic diseases research.
