TY -的盟Wakamiya Shoko AU -卡瓦依Yukiko盟——Aramaki PY - 2018 DA -二2018/9/25 TI -基于流感检测流感高峰后通过微博和间接信息:文本挖掘研究乔- JMIR公共卫生Surveill SP - e65六世- 4 - 3 KW -流感监测KW -位置提到KW - Twitter KW -社交网络KW -空间分析KW -互联网KW -微博KW - infodemiology KW - infoveillance AB -背景:最近社交网络服务(sns)的普及和规模的增长导致了对基于sns的信息提取系统的需求的增加。SNS数据的一个热门应用是健康监测，通过SNS平台上发布的短信检测疾病，预测流行病的爆发。这类应用的逻辑如下:它们将SNS用户作为社交传感器。这些基于社交传感器的方法也有一个共同的问题:如果有足够多的活跃用户，基于社交网络的监控就会更加可靠，而少量或不活跃的用户会产生不一致的结果。目的:本研究提出了一种新的方法来估计病人人数的趋势，利用间接信息覆盖城市地区和农村地区的岗位。方法:通过嵌入直接信息和间接信息，建立TRAP模型。研究人员收集了3年的推文(日语中700万条与流感相关的推文)来评估该模型。直接信息和间接信息都提到了其他地方。由于间接信息不如直接信息可靠(噪声太大或太旧)，因此不直接使用间接信息数据，认为是抑制直接信息。 For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. SN - 2369-2960 UR - http://publichealth.www.mybigtv.com/2018/3/e65/ UR - https://doi.org/10.2196/publichealth.8627 UR - http://www.ncbi.nlm.nih.gov/pubmed/30274968 DO - 10.2196/publichealth.8627 ID - info:doi/10.2196/publichealth.8627 ER -