III: Small: TwitterHealth: Learning Fine-Grained Models of Health Influences and Interactions From Social Media

III：小：TwitterHealth：从社交媒体学习健康影响和互动的细粒度模型

• 批准号: 1319378
• 负责人: Henry Kautz
• 金额: $ 48.19万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319378&HistoricalAwards=false
• 关键词: III; Small; TwitterHealth; Learning; Fine

Current techniques for answering questions about the influence of behaviorial and environmental factors on public health are based on surveys, which are costly and subject to response bias, or simulations, which rely on possibly incorrect or simplistic assumptions. The TwitterHealth project is developing techniques to extract reliable public health information from social media. In essence, the online population becauses a vast organic sensor network. Statistical natural language processing techniques are employed to classify tweets (or other social media postings) as self-reports of disease or particular behaviors of interest. GPS information included in postings made from cell phones allow a variety of behavioral information to be inferred about each user, such as the venues visited and the other individuals from the data set who are encountered.Major technical challenges for using social media in this manner are the highly noisy nature of the information channel, scaling to a large number of different health conditions, and the need to discover causal influences as well as correlations between behavioral and environmental factors and health. The challenge of noise is approached by learning dynamic relational models of health states, which generalize classical epidemiological models but support individual as well as aggregate predictions. The scaling challenge is dealt with by knowledge transfer techniques, which reduce data and computational requirements by transfering information between models for different health conditions. Specific knowledge transfer techniques are cascaded training of a target classifier starting with a given classifier for a related but different disease, and the use of ensembles of general and specific classifiers. The challenge of inferring casuality is addressed by temporal-lag methods, which identify changes in behaviorial or environmental conditions that consistently precede changes in health. For example, the inference that a venue is a cause (vector) of disease spread is accomplished by tracing backward in time the GPS trails of users who post social media reports of illness. TwitterHealth employs two approaches for validating its results: first, comparing the aggregate predictions of the model against CDC statistics; second, comparing individuals' behavior in reporting or not reporting disease symptoms in status updates against the behavior predicted by the models. The project also includes planning for clinic based evaluations, in which subjects identified by their social media postings would provide swabs that would be tested for disease agents.The TwitterHealth approach to collecting and analyzing health information has the potential to improve public health, by making detailed data about health, behavior, social structure, and geographic influences available in real time and at almost no cost. While it will not completely replace traditional methods of gathering health information, it provides an important complementary information channel, which emphases speed, reach, and scale. The project includes outreach expert medical professionals in order to plan future clinical validation. The outreach interaction provides a forum for exchange of computer science and medical expertise between researchers and students in the two fields. Information about the project is available online at http://www.cs.rochester.edu/u/kautz/twitterhealth.

目前回答行为和环境因素对公共卫生影响问题的技术是基于调查，这是昂贵的，并受到反应偏差，或模拟，这依赖于可能不正确或简单化的假设。 TwitterHealth项目正在开发从社交媒体中提取可靠公共卫生信息的技术。 从本质上讲，在线人口是因为一个巨大的有机传感器网络。 采用统计自然语言处理技术将推文（或其他社交媒体帖子）分类为疾病或感兴趣的特定行为的自我报告。 通过手机发布的帖子中包含的GPS信息，可以推断出每个用户的各种行为信息，例如访问过的场所和遇到的数据集中的其他个人。以这种方式使用社交媒体的主要技术挑战是信息通道的高噪声性质，可扩展到大量不同的健康状况，以及需要发现因果影响以及行为和环境因素与健康之间的相关性。 噪声的挑战是通过学习健康状态的动态关系模型来解决的，该模型概括了经典的流行病学模型，但支持个人和总体预测。 通过知识转移技术来应对缩放挑战，该技术通过在不同健康状况的模型之间转移信息来减少数据和计算需求。 特定的知识转移技术是从相关但不同疾病的给定分类器开始的目标分类器的级联训练，以及使用一般和特定分类器的集合。 时间滞后方法解决了推断健康的挑战，该方法确定了行为或环境条件的变化，这些变化始终先于健康变化。 例如，推断一个地点是疾病传播的原因（媒介）是通过追溯发布疾病报告的社交媒体用户的GPS轨迹来实现的。 TwitterHealth采用两种方法来验证其结果：首先，将模型的总体预测与CDC统计数据进行比较;其次，将个人在状态更新中报告或不报告疾病症状的行为与模型预测的行为进行比较。 该项目还包括规划基于诊所的评估，其中通过社交媒体帖子识别的受试者将提供用于疾病病原体测试的拭子。TwitterHealth收集和分析健康信息的方法有可能改善公共健康，通过真实的时间和几乎免费提供有关健康，行为，社会结构和地理影响的详细数据。 虽然它不会完全取代收集健康信息的传统方法，但它提供了一个重要的补充信息渠道，强调速度，范围和规模。 该项目包括外展专家医疗专业人员，以规划未来的临床验证。 外展互动为这两个领域的研究人员和学生之间交流计算机科学和医学专业知识提供了一个论坛。有关该项目的信息可在http://www.cs.rochester.edu/u/kautz/twitterhealth上查阅。