2018年该主题发表的文章:30篇(向下滚动以加载其余文章)
2018
![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
![Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study 使用机器学习算法预测糖代谢紊乱风险:试点研究](https://asset.jmir.pub/assets/thumbs/37ed395efaab8ab5e4c488f05efc949c.png 480w,https://asset.jmir.pub/assets/thumbs/37ed395efaab8ab5e4c488f05efc949c.png 960w,https://asset.jmir.pub/assets/thumbs/37ed395efaab8ab5e4c488f05efc949c.png 1920w,https://asset.jmir.pub/assets/thumbs/37ed395efaab8ab5e4c488f05efc949c.png 2500w)
Katsutoshi Maeta,于他,Kazutoshi Fujibayashi,Toshiaki称,Noriko Sasabe,君子饭岛爱,Toshio Naito
JMIR Diabetes 2018(11月26日);3 (4): e10212
![Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review 使用卷积神经网络进行皮肤癌分类:系统综述](https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/58cfaf55ebc4af87e1a916614b1c6c85.png 480w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/58cfaf55ebc4af87e1a916614b1c6c85.png 960w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/58cfaf55ebc4af87e1a916614b1c6c85.png 1920w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/58cfaf55ebc4af87e1a916614b1c6c85.png 2500w)
提图斯·约瑟夫·布林克,Achim Hekler,约亨·斯文·尤蒂卡尔,尼尔斯·主观能动性,德克Schadendorf,约阿希姆Klode,卡罗拉伯克,特蕾莎Steeb,亚历山大·亨克,克里斯托夫·冯·卡莱
J Med Internet Res 2018(10月17日);20 (10): e11936
![Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches 利用电子病历和生物医学文献支持罕见病的诊断,采用数据融合和协同过滤方法](https://asset.jmir.pub/assets/thumbs/0afc1e85998a62d728f6e6172305d26f.png 480w,https://asset.jmir.pub/assets/thumbs/0afc1e85998a62d728f6e6172305d26f.png 960w,https://asset.jmir.pub/assets/thumbs/0afc1e85998a62d728f6e6172305d26f.png 1920w,https://asset.jmir.pub/assets/thumbs/0afc1e85998a62d728f6e6172305d26f.png 2500w)
利用电子病历和生物医学文献支持罕见病的诊断,采用数据融合和协同过滤方法
Feichen沈,思嘉刘,燕山王,安德鲁·温,身子王,Hongfang刘
JMIR Med Inform 2018(10月10日);6 (4): e11301
![Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial 预测心肌梗死后抑郁和焦虑症状的网络心理治疗依从性:来自U-CARE心脏随机对照试验的机器学习见解](https://asset.jmir.pub/assets/thumbs/ef50ea75757c65a82ea66292e80a14e3.png 480w,https://asset.jmir.pub/assets/thumbs/ef50ea75757c65a82ea66292e80a14e3.png 960w,https://asset.jmir.pub/assets/thumbs/ef50ea75757c65a82ea66292e80a14e3.png 1920w,https://asset.jmir.pub/assets/thumbs/ef50ea75757c65a82ea66292e80a14e3.png 2500w)
预测心肌梗死后抑郁和焦虑症状的网络心理治疗依从性:来自U-CARE心脏随机对照试验的机器学习见解
约翰Wallert,Emelie Gustafson,克拉斯举行,家伙麦迪逊,Fredrika Norlund,路易丝·冯·埃森,埃里克·马丁·古斯塔夫·奥尔森
医学互联网研究2018(10月10日);20 (10): e10754
![Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data 改善慢性阻塞性肺疾病住院风险预测:机器学习在远程监测数据中的应用](https://asset.jmir.pub/assets/thumbs/9c46ff50a15f151a4b5cbace7439b5f4.png 480w,https://asset.jmir.pub/assets/thumbs/9c46ff50a15f151a4b5cbace7439b5f4.png 960w,https://asset.jmir.pub/assets/thumbs/9c46ff50a15f151a4b5cbace7439b5f4.png 1920w,https://asset.jmir.pub/assets/thumbs/9c46ff50a15f151a4b5cbace7439b5f4.png 2500w)
改善慢性阻塞性肺疾病住院风险预测:机器学习在远程监测数据中的应用
彼得果园,安娜Agakova,希拉里小桥,克里斯托弗·大卫·伯顿,克利斯朵夫Sarran,Felix Agakov,布莱恩·麦金
医学互联网研究2018(9月21日);20 (9): e263
![Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study 无监督机器学习在基于人群的调查中识别痴呆症的高可能性:开发和验证研究](https://asset.jmir.pub/assets/thumbs/511bf3c598d92113e8adec9748bb098b.png 480w,https://asset.jmir.pub/assets/thumbs/511bf3c598d92113e8adec9748bb098b.png 960w,https://asset.jmir.pub/assets/thumbs/511bf3c598d92113e8adec9748bb098b.png 1920w,https://asset.jmir.pub/assets/thumbs/511bf3c598d92113e8adec9748bb098b.png 2500w)
无监督机器学习在基于人群的调查中识别痴呆症的高可能性:开发和验证研究
Laurent Cleret de Langavant,Eleonore拜仁,Kristine Yaffe
J Med Internet Res 2018(09年7月);20 (7): e10493
![Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models 2015年麻疹爆发期间推文的公众感知分析:使用卷积神经网络模型的比较研究](https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/9e59c8156cda79205dd859548385f30f.png 480w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/9e59c8156cda79205dd859548385f30f.png 960w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/9e59c8156cda79205dd859548385f30f.png 1920w,https://s3.ca-central-1.amazonaws.com/assets.www.mybigtv.com/assets/9e59c8156cda79205dd859548385f30f.png 2500w)
2015年麻疹爆发期间推文的公众感知分析:使用卷积神经网络模型的比较研究
精诚杜,陆唐,杨香,Degui智,徐俊,Hsing-Yi歌,崔道
J Med Internet Res 2018(09年7月);20 (7): e236
![A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study 从生物医学文献中自动识别科学严谨临床研究报告的深度学习方法:比较分析研究](https://asset.jmir.pub/assets/thumbs/b0bbc7d94142b03c2a34f7cba9d36ec0.png 480w,https://asset.jmir.pub/assets/thumbs/b0bbc7d94142b03c2a34f7cba9d36ec0.png 960w,https://asset.jmir.pub/assets/thumbs/b0bbc7d94142b03c2a34f7cba9d36ec0.png 1920w,https://asset.jmir.pub/assets/thumbs/b0bbc7d94142b03c2a34f7cba9d36ec0.png 2500w)
从生物医学文献中自动识别科学严谨临床研究报告的深度学习方法:比较分析研究
Guilherme Del Fiol,马修·迈克尔逊,阿方索人工,克里斯Cotoi,布莱恩·海恩斯报道
J Med Internet Res 2018(6月25日);20 (6): e10281
![Using Neural Networks with Routine Health Records to Identify Suicide Risk: Feasibility Study 使用神经网络与常规健康记录识别自杀风险:可行性研究](https://asset.jmir.pub/assets/thumbs/dfd3c26521a500cc0afa353a08158e10.png 480w,https://asset.jmir.pub/assets/thumbs/dfd3c26521a500cc0afa353a08158e10.png 960w,https://asset.jmir.pub/assets/thumbs/dfd3c26521a500cc0afa353a08158e10.png 1920w,https://asset.jmir.pub/assets/thumbs/dfd3c26521a500cc0afa353a08158e10.png 2500w)
马科斯DelPozo-Banos,约翰安,尼科莱Petkov,达蒙·马克·贝里奇,凯特南部,基思·劳埃德,卡洛琳琼斯,莎拉·斯宾塞,卡洛斯·曼纽尔·特拉维索
JMIR Ment Health 2018(6月22日);5 (2): e10144
![Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum 及时发现恢复问题:自动语言分析和监督机器学习在在线药物滥用论坛上的应用](https://asset.jmir.pub/assets/thumbs/eb7b2d90bae6511980a8a553083cd9be.png 480w,https://asset.jmir.pub/assets/thumbs/eb7b2d90bae6511980a8a553083cd9be.png 960w,https://asset.jmir.pub/assets/thumbs/eb7b2d90bae6511980a8a553083cd9be.png 1920w,https://asset.jmir.pub/assets/thumbs/eb7b2d90bae6511980a8a553083cd9be.png 2500w)
及时发现恢复问题:自动语言分析和监督机器学习在在线药物滥用论坛上的应用
瑞秋本非常优秀,Prathusha K Sarma,达万V沙阿,菲奥娜McTavish,吉娜Landucci,Klaren Pe-Romashko,大卫·H·古斯塔夫森
J Med Internet Res 2018(6月12日);20 (6): e10136
![Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning 预测客户投诉的原因:用机器学习预测体外诊断分析质量问题的第一步](https://asset.jmir.pub/assets/thumbs/73713ba0253f9053b3ee60305c31d0dc.png 480w,https://asset.jmir.pub/assets/thumbs/73713ba0253f9053b3ee60305c31d0dc.png 960w,https://asset.jmir.pub/assets/thumbs/73713ba0253f9053b3ee60305c31d0dc.png 1920w,https://asset.jmir.pub/assets/thumbs/73713ba0253f9053b3ee60305c31d0dc.png 2500w)
预测客户投诉的原因:用机器学习预测体外诊断分析质量问题的第一步
Stephane Aris-Brosou,詹姆斯•金,丽丽,回族刘
JMIR Med Inform 2018(5月15日);6 (2): e34