Digital health · 2025
Personalized insights into urinary tract infection management: A text mining analysis of online consultation data.
СтатьяНазвание и аннотация: Перевод в очереди
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Tang Ruijie, Zhu Peiqi, Yan Ruxue, Zhou Yaping, Tang Zhian, He Weiming
Аннотация
OBJECTIVES: Urinary tract infections (UTIs) frequently affect individuals of all ages, necessitating antibiotic treatment and medical care, which can impair quality of life and cause psychological strain. Online Health Consultation (OHC) platforms serve as a widely used communication tool, offering integrated support for medical guidance and disease management. By examining OHC interactions, this study explores the concerns and difficulties experienced by UTI patients to better understand their perspectives.
METHODS: Data from 20,000 anonymized UTI-related records (2020-2024) were obtained from a major Chinese online healthcare platform, Good Doctor Online. Analysis occurred in two stages: BERTopic extracted key themes and keywords from text data, followed by sentiment analysis of these findings using a generative AI language model. All data was publicly accessible and de-identified.
RESULTS: Analysis of 18,479 cleaned records using BERTopic identified six key themes: "Polite Expressions for Consultation," "Symptom and Management Challenges," "Differential Diagnosis of Cystitis," "Etiology Related to Sexual Activity," "Nocturnal Symptoms and Fever," and "Perinatal Considerations." Sentiment analysis showed predominantly negative emotions, reflecting the condition's substantial physical and mental toll. The "Etiology Related to Sexual Activity" theme had the highest negativity (97%), while "Polite Expressions for Consultation" showed the most positivity (9%).
CONCLUSION: These research results highlight the important role of online communities in providing support and information to patients, and the insights derived from this study can provide valuable reference for social media developers, medical service providers, and policymakers.
METHODS: Data from 20,000 anonymized UTI-related records (2020-2024) were obtained from a major Chinese online healthcare platform, Good Doctor Online. Analysis occurred in two stages: BERTopic extracted key themes and keywords from text data, followed by sentiment analysis of these findings using a generative AI language model. All data was publicly accessible and de-identified.
RESULTS: Analysis of 18,479 cleaned records using BERTopic identified six key themes: "Polite Expressions for Consultation," "Symptom and Management Challenges," "Differential Diagnosis of Cystitis," "Etiology Related to Sexual Activity," "Nocturnal Symptoms and Fever," and "Perinatal Considerations." Sentiment analysis showed predominantly negative emotions, reflecting the condition's substantial physical and mental toll. The "Etiology Related to Sexual Activity" theme had the highest negativity (97%), while "Polite Expressions for Consultation" showed the most positivity (9%).
CONCLUSION: These research results highlight the important role of online communities in providing support and information to patients, and the insights derived from this study can provide valuable reference for social media developers, medical service providers, and policymakers.