What can we learn from a Chinese social media used by glaucoma patients?
Abstract 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). Methods In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glauco...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-11-01
|
| Series: | BMC Ophthalmology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12886-023-03208-5 |
| _version_ | 1827635167501484032 |
|---|---|
| author | Junxia Fu Junrui Yang Qiuman Li Danqing Huang Hongyang Yang Xiaoling Xie Huaxin Xu Mingzhi Zhang Ce Zheng |
| author_facet | Junxia Fu Junrui Yang Qiuman Li Danqing Huang Hongyang Yang Xiaoling Xie Huaxin Xu Mingzhi Zhang Ce Zheng |
| author_sort | Junxia Fu |
| collection | DOAJ |
| description | Abstract 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). Methods In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba, China. According to the contents of topic posts, we classified them into posts with seeking medical advice and without seeking medical advice (social support, expressing emotions, sharing knowledge, and others). Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords of topic posts. 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, 64.91%), and seeking advice regarding symptoms or examination (N = 4913, 45.11%) dominated the majority. The following were searching for social support (N = 2362, 21.69%), expressing emotions (N = 497, 4.56%), and sharing knowledge (N = 527, 4.84%) in sequence. The word cloud analysis results showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for the BERT model, 0.82, 0.821, and 0.890 for the Bi-LSTM model. Conclusion Social media can help enhance the patient-doctor relationship by providing patients’ concerns and cognition about glaucoma in China. NLP can be a powerful tool to reflect patients’ focus on diseases. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring. |
| first_indexed | 2024-03-09T15:23:27Z |
| format | Article |
| id | doaj.art-796826e0427e4e9baa1c7084ab9a38c8 |
| institution | Directory Open Access Journal |
| issn | 1471-2415 |
| language | English |
| last_indexed | 2024-03-09T15:23:27Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Ophthalmology |
| spelling | doaj.art-796826e0427e4e9baa1c7084ab9a38c82023-11-26T12:40:15ZengBMCBMC Ophthalmology1471-24152023-11-012311910.1186/s12886-023-03208-5What can we learn from a Chinese social media used by glaucoma patients?Junxia Fu0Junrui Yang1Qiuman Li2Danqing Huang3Hongyang Yang4Xiaoling Xie5Huaxin Xu6Mingzhi Zhang7Ce Zheng8Department of Ophthalmology, School of Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong UniversityJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical CollegeDepartment of Pediatric Cardiology, Guangzhou Women and Children’s Medical CenterInstitute of Hospital Development Strategy, China Hospital Development Institute, Shanghai Jiao Tong UniversityInstitute of Hospital Development Strategy, China Hospital Development Institute, Shanghai Jiao Tong UniversityJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical CollegeThe Faculty of Science, University of Technology SydneyJoint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical CollegeDepartment of Ophthalmology, School of Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong UniversityAbstract 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). Methods In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba, China. According to the contents of topic posts, we classified them into posts with seeking medical advice and without seeking medical advice (social support, expressing emotions, sharing knowledge, and others). Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords of topic posts. 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, 64.91%), and seeking advice regarding symptoms or examination (N = 4913, 45.11%) dominated the majority. The following were searching for social support (N = 2362, 21.69%), expressing emotions (N = 497, 4.56%), and sharing knowledge (N = 527, 4.84%) in sequence. The word cloud analysis results showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for the BERT model, 0.82, 0.821, and 0.890 for the Bi-LSTM model. Conclusion Social media can help enhance the patient-doctor relationship by providing patients’ concerns and cognition about glaucoma in China. NLP can be a powerful tool to reflect patients’ focus on diseases. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring.https://doi.org/10.1186/s12886-023-03208-5GlaucomaSocial mediaWord cloud analysis |
| spellingShingle | Junxia Fu Junrui Yang Qiuman Li Danqing Huang Hongyang Yang Xiaoling Xie Huaxin Xu Mingzhi Zhang Ce Zheng What can we learn from a Chinese social media used by glaucoma patients? BMC Ophthalmology Glaucoma Social media Word cloud analysis |
| title | What can we learn from a Chinese social media used by glaucoma patients? |
| title_full | What can we learn from a Chinese social media used by glaucoma patients? |
| title_fullStr | What can we learn from a Chinese social media used by glaucoma patients? |
| title_full_unstemmed | What can we learn from a Chinese social media used by glaucoma patients? |
| title_short | What can we learn from a Chinese social media used by glaucoma patients? |
| title_sort | what can we learn from a chinese social media used by glaucoma patients |
| topic | Glaucoma Social media Word cloud analysis |
| url | https://doi.org/10.1186/s12886-023-03208-5 |
| work_keys_str_mv | AT junxiafu whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT junruiyang whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT qiumanli whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT danqinghuang whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT hongyangyang whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT xiaolingxie whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT huaxinxu whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT mingzhizhang whatcanwelearnfromachinesesocialmediausedbyglaucomapatients AT cezheng whatcanwelearnfromachinesesocialmediausedbyglaucomapatients |