2018年该主题发表的文章:57篇(向下滚动以加载其余文章)
2018
![Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study Twitter上关于高血压和糖尿病隐喻的内容分析:探索性混合方法研究](https://asset.jmir.pub/assets/thumbs/8db82edd74340d0d82f58293e3a6fb9e.png 480w,https://asset.jmir.pub/assets/thumbs/8db82edd74340d0d82f58293e3a6fb9e.png 960w,https://asset.jmir.pub/assets/thumbs/8db82edd74340d0d82f58293e3a6fb9e.png 1920w,https://asset.jmir.pub/assets/thumbs/8db82edd74340d0d82f58293e3a6fb9e.png 2500w)
Twitter上关于高血压和糖尿病隐喻的内容分析:探索性混合方法研究
劳伦Sinnenberg,克里斯蒂娜Mancheno,弗朗西斯·K·巴格,大卫·阿施,克里斯蒂·李·里瓦德,艾玛Horst-Martz,艾莉森Buttenheim,安格莱尔,蕾娜的商人
JMIR Diabetes 2018(12月21日);3 (4): e11177
![Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study 结合机器学习方法的医院大数据实时流感监测:对比研究](https://asset.jmir.pub/assets/thumbs/6a0ff1f7ca0571210472fde8d6f7aec8.png 480w,https://asset.jmir.pub/assets/thumbs/6a0ff1f7ca0571210472fde8d6f7aec8.png 960w,https://asset.jmir.pub/assets/thumbs/6a0ff1f7ca0571210472fde8d6f7aec8.png 1920w,https://asset.jmir.pub/assets/thumbs/6a0ff1f7ca0571210472fde8d6f7aec8.png 2500w)
Canelle地方,奥黛丽Lavenu,瓦莱丽Bertaud,鲍里斯Campillo-Gimenez,伊曼纽尔Chazard,Marc Cuggia,Guillaume Bouzille
JMIR公共卫生监测2018(12月21日);4 (4): e11361
![Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram 探索社区生成的社交媒体内容对抑郁症检测的效用:对Instagram的分析研究](https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/1719dead16ebae60589807b93cf2101d.png 480w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/1719dead16ebae60589807b93cf2101d.png 960w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/1719dead16ebae60589807b93cf2101d.png 1920w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/1719dead16ebae60589807b93cf2101d.png 2500w)
探索社区生成的社交媒体内容对抑郁症检测的效用:对Instagram的分析研究
本杰明·J·理查德,Lisa A Marsch,主人便雅悯,赛义德娜斯
J Med Internet Res 2018(12月6日);20 (12): e11817
![Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis 表征推特的数量和内容关于宾夕法尼亚州常见的健康状况:回顾性分析](https://asset.jmir.pub/assets/thumbs/3f875a3d9d1e8ad6e3f46cf295af8f60.png 480w,https://asset.jmir.pub/assets/thumbs/3f875a3d9d1e8ad6e3f46cf295af8f60.png 960w,https://asset.jmir.pub/assets/thumbs/3f875a3d9d1e8ad6e3f46cf295af8f60.png 1920w,https://asset.jmir.pub/assets/thumbs/3f875a3d9d1e8ad6e3f46cf295af8f60.png 2500w)
表征推特的数量和内容关于宾夕法尼亚州常见的健康状况:回顾性分析
克里斯托弗·塔夫茨,丹尼尔Polsky,凯文·G·沃尔普,彼得·W·格林内菲尔德,安格莱尔,Raina M Merchant,亚瑟·P·佩鲁洛
JMIR公共卫生监测2018(12月6日);4 (4): e10834
![Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum 将患者生成的数据聚类到可操作主题的新方法:基于网络的乳腺癌论坛的案例研究](https://asset.jmir.pub/assets/thumbs/ad300db9ad9c347979c1d1c4eef801d8.png 480w,https://asset.jmir.pub/assets/thumbs/ad300db9ad9c347979c1d1c4eef801d8.png 960w,https://asset.jmir.pub/assets/thumbs/ad300db9ad9c347979c1d1c4eef801d8.png 1920w,https://asset.jmir.pub/assets/thumbs/ad300db9ad9c347979c1d1c4eef801d8.png 2500w)
将患者生成的数据聚类到可操作主题的新方法:基于网络的乳腺癌论坛的案例研究
Josette琼斯,Meeta普拉丹,Masoud Hosseini,Anand Kulanthaivel,马哈茂德Hosseini
JMIR Med Inform 2018(11月29日);6 (4): e45
![Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic 突发公共卫生事件中卫生机构反应和公众参与的动态——以2016年寨卡疫情期间CDC推特模式为例](https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/8bcaaec81e8e058820ce9c3ed7da226a.png 480w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/8bcaaec81e8e058820ce9c3ed7da226a.png 960w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/8bcaaec81e8e058820ce9c3ed7da226a.png 1920w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/8bcaaec81e8e058820ce9c3ed7da226a.png 2500w)
突发公共卫生事件中卫生机构反应和公众参与的动态——以2016年寨卡疫情期间CDC推特模式为例
陈释,钱徐,约翰Buchenberger,Arunkumar Bagavathi,加布里埃尔公平,莎米拉谢赫,哈斯。克里希南
JMIR公共卫生监测2018(11月22日);4 (4): e10827
![How Twitter Can Support the HIV/AIDS Response to Achieve the 2030 Eradication Goal: In-Depth Thematic Analysis of World AIDS Day Tweets 推特如何支持应对艾滋病毒/艾滋病以实现2030年根除目标:对世界艾滋病日推文的深入专题分析](https://asset.jmir.pub/assets/thumbs/6fe72916ac2388cfca14fef7cabec338.png 480w,https://asset.jmir.pub/assets/thumbs/6fe72916ac2388cfca14fef7cabec338.png 960w,https://asset.jmir.pub/assets/thumbs/6fe72916ac2388cfca14fef7cabec338.png 1920w,https://asset.jmir.pub/assets/thumbs/6fe72916ac2388cfca14fef7cabec338.png 2500w)
推特如何支持应对艾滋病毒/艾滋病以实现2030年根除目标:对世界艾滋病日推文的深入专题分析
米歇尔Odlum,Sunmoo Yoon,彼得Broadwell,拉塞尔•布鲁尔,Da旷
JMIR公共卫生监测2018(11月22日);4 (4): e10262
![Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification Instagram上水烟(水管)的自动识别:基于卷积神经网络和支持向量机分类的特征提取应用](https://asset.jmir.pub/assets/thumbs/cdac13ae224fe5060d6c20ff89c879d7.png 480w,https://asset.jmir.pub/assets/thumbs/cdac13ae224fe5060d6c20ff89c879d7.png 960w,https://asset.jmir.pub/assets/thumbs/cdac13ae224fe5060d6c20ff89c879d7.png 1920w,https://asset.jmir.pub/assets/thumbs/cdac13ae224fe5060d6c20ff89c879d7.png 2500w)
Instagram上水烟(水管)的自动识别:基于卷积神经网络和支持向量机分类的特征提取应用
Youshan张,Jon-Patrick Allem,詹妮弗·贝丝·昂格,苔丝·波莉·克鲁兹
医学互联网研究2018(11月21日);20 (11): e10513
![Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis 基于网络的信号检测使用法国医学论坛数据:比较分析](https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/496cd17004003ed7c0e56bfee251c88e.png 480w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/496cd17004003ed7c0e56bfee251c88e.png 960w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/496cd17004003ed7c0e56bfee251c88e.png 1920w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/496cd17004003ed7c0e56bfee251c88e.png 2500w)
中,Kurzinger,Stephane舒克,娜塔莉Texier,Redhouane Abdellaoui,卡罗尔Faviez,朱莉Pouget,凌张,斯蒂芬妮Tcherny-Lessenot,斯蒂芬•林,Juhaeri Juhaeri
J Med Internet Res 2018(11月20日);20 (11): e10466
![Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis 2017 - 2018年水烟相关推文的主题分析](https://asset.jmir.pub/assets/thumbs/5dcd4b2314eb5b8fe0bd13c35f6f1994.png 480w,https://asset.jmir.pub/assets/thumbs/5dcd4b2314eb5b8fe0bd13c35f6f1994.png 960w,https://asset.jmir.pub/assets/thumbs/5dcd4b2314eb5b8fe0bd13c35f6f1994.png 1920w,https://asset.jmir.pub/assets/thumbs/5dcd4b2314eb5b8fe0bd13c35f6f1994.png 2500w)
Jon-Patrick Allem,Likhit Dharmapuri,Adam M . Leventhal,詹妮弗·B·昂格,苔丝·波莉·克鲁兹
医学互联网研究2018(11月19日);20 (11): e11669
![Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study 识别和理解使用Twitter联系抑郁症的社区:横断面研究](https://asset.jmir.pub/assets/thumbs/f6a335d316ae4c795405f1640055e57c.png 480w,https://asset.jmir.pub/assets/thumbs/f6a335d316ae4c795405f1640055e57c.png 960w,https://asset.jmir.pub/assets/thumbs/f6a335d316ae4c795405f1640055e57c.png 1920w,https://asset.jmir.pub/assets/thumbs/f6a335d316ae4c795405f1640055e57c.png 2500w)
Amber D. DeJohn,艾米丽·英格利希·舒尔茨,安珀·L·皮尔森,Megan Lachmar报道,Andrea K Wittenborn
JMIR Ment Health 2018(05年11月);5 (4): e61
![Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study 在美国使用Twitter检查基于网络的患者体验情绪:纵向研究](https://asset.jmir.pub/assets/thumbs/3710ecdbaa7276ef8b0b31bcfd9a6a43.png 480w,https://asset.jmir.pub/assets/thumbs/3710ecdbaa7276ef8b0b31bcfd9a6a43.png 960w,https://asset.jmir.pub/assets/thumbs/3710ecdbaa7276ef8b0b31bcfd9a6a43.png 1920w,https://asset.jmir.pub/assets/thumbs/3710ecdbaa7276ef8b0b31bcfd9a6a43.png 2500w)
在美国使用Twitter检查基于网络的患者体验情绪:纵向研究
卡拉·C·塞沃克,Gaurav Tuli,玉林Hswen,约翰·S·布朗斯坦,杰瑞德·霍金斯
J Med Internet Res 2018(10月12日);20 (10): e10043