What can we learn from a Chinese social media used by glaucoma patients? (original) (raw)
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Glaucoma-related posts from a Chinese social media: An exploratory study
Purpose: Our study aims to discuss glaucoma patients' needs and Internet habits using big data analysis and Natural Language Processing (NLP) based on deep learning (DL). We also developed and validated DL models to recognize social media data. Methods: In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba. According to the contents of topic posts, we classified them into posts with or without seeking medical advice. Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords. Two DL models, Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were trained to identify the posts seeking medical advice. The evaluation matrices included: accuracy, F1 value, and the area under the ROC curve (AUC). Results: A total of 10,892 topic posts were included, among them, most were seeking medical advice (N=7071, 64...
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