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Flu Gone Viral: Syndromic Surveillance of Flu on Twitter using Temporal Topic Models
"... Abstract—Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for nowcasting the flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemic ..."
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Abstract—Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for nowcasting the flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemics in a population. There is a disconnect between data-driven methods for forecasting flu incidence and epidemiological models that adopt a state based understanding of transitions, that can lead to sub-optimal predictions. Furthermore, models for epidemiological activity and social activity like on Twitter predict different shapes and have important differences. We propose a temporal topic model to capture hidden states of a user from his tweets and aggregate states in a geographical region for better estimation of trends. We show that our approach helps fill the gap between phenomenolog-ical methods for disease surveillance and epidemiological models. We validate this approach by modeling the flu using Twitter in multiple countries of South America. We demonstrate that our model can consistently outperform plain vocabulary assessment in flu case-count predictions, and at the same time get better flu-peak predictions than competitors. We also show that our fine-grained modeling can reconcile some contrasting behaviors between epidemiological and social models. I.
Noname manuscript No. (will be inserted by the editor) Syndromic Surveillance of Flu on Twitter Using Weakly Supervised Temporal Topic Models
"... Abstract Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for nowcasting the flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemic ..."
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Abstract Surveillance of epidemic outbreaks and spread from social media is an important tool for governments and public health authorities. Machine learning techniques for nowcasting the flu have made significant inroads into correlating social media trends to case counts and prevalence of epidemics in a population. There is a disconnect between data-driven methods for forecast-ing flu incidence and epidemiological models that adopt a state based under-standing of transitions, that can lead to sub-optimal predictions. Furthermore, models for epidemiological activity and social activity like on Twitter predict different shapes and have important differences. In this paper, we propose two temporal topic models (one unsupervised model as well as one improved weakly-supervised model) to capture hidden states of a user from his tweets and aggregate states in a geographical region for better estimation of trends. We show that our approaches help fill the gap between phenomenological methods for disease surveillance and epidemiolog-ical models. We validate our approaches by modeling the flu using Twitter in multiple countries of South America. We demonstrate that our models can consistently outperform plain vocabulary assessment in flu case-count predic-tions, and at the same time get better flu-peak predictions than competitors. We also show that our fine-grained modeling can reconcile some contrasting behaviors between epidemiological and social models.
wuerzburg.de
"... When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful ..."
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When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.
The Web as a Jungle: Non-Linear Dynamical Systems for Co-evolving Online Activities
"... Given a large collection of co-evolving online activities, such as ..."
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Big Data Analytics for Development: Events, Knowledge Graphs and Predictive Models
, 2015
"... Volatility in critical socio-economic indices can have a significant negative impact on global development. This thesis presents a suite of novel big data analytics algorithms that operate on unstructured Web data streams to automatically infer events, knowledge graphs and predictive models to under ..."
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Volatility in critical socio-economic indices can have a significant negative impact on global development. This thesis presents a suite of novel big data analytics algorithms that operate on unstructured Web data streams to automatically infer events, knowledge graphs and predictive models to understand, characterize and predict the volatility of socioeconomic indices. This thesis makes four important research contributions. First, given a large volume of diverse un-structured news streams, we present new models for capturing events and learning spatio-temporal char-acteristics of events from news streams. We specifically explore two types of event models in this thesis: one centered around the concept of event triggers and a probabilistic meta-event model that explicitly de-lineates named entities from text streams to learn a generic class of meta-events. The second contribution focuses on learning several different types of knowledge graphs from news streams and events: a) Spatio-temporal article graphs capture intrinsic relationships between different news articles; b) Event graphs characterize relationships between events and given a news query, provide a succinct summary of a time-line of events relating to a query; c) Event-phenomenon graphs that provide a condensed representation of classes of events that relate to a given phenomena at a given location and time; d) Causality testing on
Unsupervised Learning of Disease Progression Models
"... Chronic diseases, such as Alzheimer’s Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in ..."
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Chronic diseases, such as Alzheimer’s Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.
