Nutrition is a key factor in people's overall health. Hence, understanding the nature and dynamics of population-wide dietary preferences over time and space can be valuable in public health. To date, studies have leveraged small samples of participants via food intake logs or treatment data. We propose a complementary source of population data on nutrition obtained via Web logs. Our main contribution is a spatiotemporal analysis of population-wide dietary preferences through the lens of logs gathered by a widely distributed Web-browser add-on, using the access volume of recipes that users seek via search as a proxy for actual food consumption. We discover that variation in dietary preferences as expressed via recipe access has two main periodic components, one yearly and the other weekly, and that there exist characteristic regional differences in terms of diet within the United States. In a second study, we identify users who show evidence of having made an acute decision to lose weight. We characterize the shifts in interests that they express in their search queries and focus on changes in their recipe queries in particular. Last, we correlate nutritional time series obtained from recipe queries with time-aligned data on hospital admissions, aimed at understanding how behavioral data captured in Web logs might be harnessed to identify potential relationships between diet and acute health problems. In this preliminary study, we focus on patterns of sodium identified in recipes over time and patterns of admission for congestive heart failure, a chronic illness that can be exacerbated by increases in sodium intake.
Studies with search logs can provide valuable insights on associations between concepts @cite_7 , and previously unknown evidence of associations between nutritional deficiencies and medical conditions can be mined from the medical literature @cite_9 @cite_4 . Researchers have studied trends over short periods of time to learn about the behavior of the querying population at large @cite_14 , or clustered terms by temporal frequency to understand daily or weekly variations @cite_35 . Temporal trends and periodicities in longer-term query volume have been leveraged in approaches that aggregate data at the user @cite_30 or the population level @cite_24 . @cite_25 proposed methods for discovering semantically similar queries by identifying queries with similar demand patterns over time. More recently, @cite_34 predict time-varying user behavior using smoothing and trends and explore other dynamics of Web behaviors, such as the detection of periodicities and surprises. Particularly relevant here is research on the prediction of disease epidemics using logs; e.g., @cite_10 used query logs as a form of surveillance for early detection of influenza. Known seasonal variations in influenza outbreaks also visible in the search logs play an important part in their predictions.
@cite_30 @cite_35 @cite_14 @cite_4 @cite_7 @cite_9 @cite_24 @cite_34 @cite_10 @cite_25
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User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012–13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance. The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine (http: search.yahoo.com) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P<.001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P<.001). Search-term surveillance may provide an additional tool for disease surveillance. In this article, we demonstrate the value of long-term query logs. Most work on query logs to date considers only short-term (within-session) query information. In contrast, we show that long-term query logs can be used to learn about the world we live in. There are many applications of this that lead not only to improving the search engine for its users, but also potentially to advances in other disciplines such as medicine, sociology, economics, and more. In this article, we will show how long-term query logs can be used for these purposes, and that their potential is severely reduced if the logs are limited to short time horizons. We show that query effects are long-lasting, provide valuable information, and might be used to automatically make medical discoveries, build concept hierarchies, and generally learn about the sociological behavior of users. We believe these applications are only the beginning of what can be done with the information contained in long-term query logs, and see this work as a step toward unlocking their potential. Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A "flu infection" concept is formulated and used to reduce spurious and potentially confounding features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12 and 28.7 , respectively.