A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> The Program for Monitoring Emerging Diseases (ProMED-mail) reports on outbreaks of diseases of infectious or toxic etiology, whether natural or intentionally released, that affect humans. Open to all sources, free of political constraints, and available to users free of charge, ProMED-mail exploits the speed and ubiquity of the Internet to serve as an early warning system for the detection of emerging disease outbreaks. ProMED-mail monitors diseases of plants and animals of agricultural importance in addition to zoonoses because of their key role in disease emergence. Reports are carefully screened for validity and are placed in an appropriate context by commentary and references from a panel of expert moderators. Reports are then posted to the Web and sent by e-mail to >32,000 subscribers in >150 countries. <s> BIB001 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> 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. <s> BIB002 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users. <s> BIB003 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> BackgroundAutomated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.ResultsGiven that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon.ConclusionThe results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting. <s> BIB004 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> Text mining for global health surveillance is an emerging technology that is gaining increased attention from public health organisations and governments. The lack of multilingual resources such as WordNets specifically targeted at this task have so far been a major bottleneck. This paper reports on a major upgrade to the BioCaster Web monitoring system and its freely available multilingual ontology; improving its original design and extending its coverage of diseases from 70 to 336 in 12 languages. <s> BIB005 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62% and a recall of 75% evaluated using manually annotated, real-world data. <s> BIB006 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> We present a general methodology for inferring the occurrence and magnitude of an event or phenomenon by exploring the rich amount of unstructured textual information on the social part of the Web. Having geo-tagged user posts on the microblogging service of Twitter as our input data, we investigate two case studies. The first consists of a benchmark problem, where actual levels of rainfall in a given location and time are inferred from the content of tweets. The second one is a real-life task, where we infer regional Influenza-like Illness rates in the effort of detecting timely an emerging epidemic disease. Our analysis builds on a statistical learning framework, which performs sparse learning via the bootstrapped version of LASSO to select a consistent subset of textual features from a large amount of candidates. In both case studies, selected features indicate close semantic correlation with the target topics and inference, conducted by regression, has a significant performance, especially given the short length --approximately one year-- of Twitter’s data time series. <s> BIB007 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Web Mining Solutions for Disease Outbreaks Detection <s> The emergence and ubiquity of online social networks have enriched web data with evolving interactions and communities both at mega-scale and in real-time. This data offers an unprecedented opportunity for studying the interaction between society and disease outbreaks. The challenge we describe in this data paper is how to extract and leverage epidemic outbreak insights from massive amounts of social media data and how this exercise can benefit medical professionals, patients, and policymakers alike. We attempt to prepare the research community for this challenge with four datasets. Publishing the four datasets will commoditize the data infrastructure to allow a higher and more efficient focal point for the research community. <s> BIB008
The problem of detecting and tracking epidemic outbreaks through social media can be defined as the task of extracting relevant knowledge about the epidemics in the real world given a stream of textual or multimedia data from social media. Web mining is the application of data mining techniques to discover and retrieve useful knowledge from the web documents and services. Therefore, the application of these techniques to knowledge extraction provides a better using and understanding of the data space on biomedical and health care domain BIB008 . There are several health data sources very useful to detect and prevent new outbreaks of different diseases. Social web media and web sites give a large amount of useful data for this purpose. Other important data sources are search engines such as Google and Yahoo! BIB003 BIB002 . In this case, the objective is to detect specific searches that involve terms that indicate influenza-like-illness (ILI) through the keywords of the queries performed. The complexity is to interpret the search context of the query, as the user may query about a particular drug, symptom or illness for a variety of reasons. Finally, ProMED-mail BIB001 is also a widely data source used for disease outbreaks detection. It is a human network of expert volunteers operating 24/7 as an official program of the International Society for Infectious Diseases. Their volunteers monitor global media reports and in many cases have a reporting time of outbreak disease alerts better than WHO reports. Text mining techniques have been applied on biomedical text corpus to named entity recognition, text classification, terminology extraction, or relationship extraction  . These methods are a human language processing algorithms that aim to convert unstructured textual data from large-scale collections to a specific format filtering them according to the needs. Once the data have been extracted from the social media sites (RSS feeds, WWW, social networks, ProMED-mail, search engines, etc...), the next stage is to perform the text analysis methods for the trend detection, identifying potential sources of disease outbreaks. These methods can be used to detect words related to diseases or their symptoms in published texts BIB007 . But this goal can be difficult because the same word can refer to a different thing depending upon context. Furthermore, a specific disease can have multiple names and symptoms associated which increases the complexity of the problem. Ontologies can help to automate human understanding of key concepts and relations between them and allow that a level of filtering accuracy can be achieved. Biomedical ontologies contain lists of terms and their human definitions, which are then given unique identifiers and classified into classes with common properties according to the specific domain treated. In the domain of EI it is necessary to identify and link term classes such as disease, symptom and species in order to detect potential focus diseases. Currently there are various available ontologies that contain all the biomedical terms necessary. For example, BioCaster ontology (BCO) BIB005 is in the OWL Semantic Web language to support automated reasoning across terms in 12 languages. A new unsupervised machine learning approach to detect public health events is proposed in Fisichella et al. work BIB006 which can complement existing systems since it allows to identify public health events (PHE) even if no matching keywords or linguistic patterns can be found. This new approach defines a generative model for predictive event detection from document by modeling the features based on trajectory distributions. Discovering time and location of the text is the value added by EI systems for high quality. In practice location names are often highly ambiguous because geotemporal disambiguation is so difficult, and because of the variety of ways in which cases are described across different texts. Keller et al. BIB004 work provides a review of the issues for epidemic surveillance and present a new method for tackling the identification of a disease outbreak location based on neural networks trained on surface feature patterns in a window around geo-entity expressions. Finally, a different solution for outbreak detection is shown in Leskovec et al paper  , where the problem is modelled as a network in order to detect the spreading Monitoring system of influenza of the virus or disease as quickly as possible. They present a new methodology for selecting nodes to detect outbreaks of dynamic processes spreading over a graph. This work shows that many objective functions for detecting outbreaks in networks, such as detection time, likelihood, and population affected, are submodular. This means that, for instance, reading only a few blogs provides more new information than reading it after we have read many ones. They use this characteristic to develop an efficient approximation algorithm (CELF) which achieves near-optimal solutions and it is 700 times faster than a simple greedy algorithm.