Twitter健康监测(THS)系统。

Manuel Rodríguez-Martínez, Cristian C Garzón-Alfonso
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引用次数: 10

摘要

我们提出了Twitter健康监测(THS)应用框架。THS被设计成一个集成平台,帮助卫生官员收集推文,确定它们是否与医疗状况有关,从中提取元数据,并创建一个可用于进一步分析数据的大数据仓库。THS建立在开源工具之上,提供以下增值服务:数据采集、Tweet分类和大数据仓库。为了验证THS,我们创建了一个大约一万二千条带标签推文的集合。这些tweet包含一个或多个目标医学术语,标签指示tweet是否与医疗状况相关。我们使用这个集合来测试基于LSTM和GRU递归神经网络的各种模型。我们的实验表明,我们可以以96%的准确率,92%的召回率和91%的F1分数对tweet进行分类。这些结果与该领域最近的研究结果相比较,表明了我们的三步走系统的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Twitter Health Surveillance (THS) System.

Twitter Health Surveillance (THS) System.

Twitter Health Surveillance (THS) System.

Twitter Health Surveillance (THS) System.

We present the Twitter Health Surveillance (THS) application framework. THS is designed as an integrated platform to help health officials collect tweets, determine if they are related with a medical condition, extract metadata out of them, and create a big data warehouse that can be used to further analyze the data. THS is built atop open source tools and provides the following value added services: Data Acquisition, Tweet Classification, and Big Data Warehousing. In order to validate THS, we have created a collection of roughly twelve thousands labelled tweets. These tweets contain one or more target medical terms, and the labels indicate if the tweet is related or not to a medical condition. We used this collection to test various models based on LSTM and GRU recurrent neural networks. Our experiments show that we can classify tweets with 96% precision, 92% recall, and 91% F1 score. These results compare favorably with recent research on this area, and show the promise of our THS system.

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