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Updated: Jul 10, 2025

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

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我们可以从中国的社交媒体中学到什么,它被绿眼病患者使用?

Junxia Fu^1,2, Junrui Yang^3,4, Qiuman Li⁵

¹Department of Ophthalmology, School of Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University, 200092, Shanghai, China.

BMC ophthalmology

|November 21, 2023

概括

玻璃眼患者经常在网上寻求医疗咨询,特别是在症状和检查方面. 自然语言处理 (NLP) 和深度学习 (DL) 模型有效地分析来自社交媒体的患者关注,以获得公共卫生洞察力.

科学领域:

医疗信息学医疗信息学
公共卫生公共卫生
计算语言学计算语言学

背景情况:

了解患者的需求和在线行为对于改善青光眼的护理至关重要.
大数据分析和自然语言处理 (NLP) 提供了从大型文本数据集中获得洞察力的新方法.

研究的目的:

用大数据和NLP与深度学习 (DL) 来分析眼患者的需求和互联网习惯.
为了在线格劳科马讨论中确定关键主题和患者关注点.

主要方法:

在Baidu Tieba上使用网络爬虫对10,892个与眼相关的帖子进行了回顾性分析.
将帖子分类为寻求医疗建议与社会支持,情感表达或知识共享.
应用双向长短期记忆 (Bi-LSTM) 和BERT深度学习模型进行文本分类,以精度,F1分数和AUC进行评估.

主要成果:

大多数职位 (64.91%) 涉及寻求医疗咨询,主要关注症状和检查 (45.11%).
社会支持 (21.69%),情感表达 (4.56%) 和知识共享 (4.84%) 构成了剩余的帖子类别.
词云分析强调"眼睛压力"",视野"",检查"和"手术"作为频繁的关键词. 与Bi-LSTM相比,BERT实现了更高的性能 (准确度:0.891,F1:0.891,AUC:0.931).与Bi-LSTM相比,BERT实现了更高的性能 (准确度:0.891,F1:0.891,AUC:0.931).

结论:

社交媒体平台可以通过揭示患者的担忧和对中国绿眼的理解来增强患者与医生的沟通.
NLP是一种有价值的工具,可以反映患者对特定疾病的关注.
深度学习模型在分类中国医学文本方面表现强,支持公共卫生监测工作.

关键词:

眼光障碍眼光障碍眼光障碍眼光障碍社交媒体社交媒体词语云分析词语云分析

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