Understanding Socio-Cultural Factors related to Obesity: Sentiment Analysis on related Tweets

Obesity can lead to the death of at least 2.8 million people each year1, yet the rate of obesity around the world has continuously increased over the past 30 years1. Societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic2.

June 18, 2019

Towards Obesity Surveillance Using Multifaceted Online Social Relational Factors in Reddit

Overweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence.

June 18, 2019

Leveraging Discussions on Reddit for Disease Surveillance

In recent years, individuals have been using social network sites like Facebook, Twitter, and Reddit to discuss health-related topics. These social media platforms consequently became new avenues for research and applications for researchers, for instance disease surveillance. Reddit, in particular, can potentially provide more in-depth contextual insights compared to Twitter, and Reddit members discuss potentially more diverse topics than Facebook members. However, identifying relevant discussions remains a challenge in large datasets like Reddit.

January 21, 2018

Opioid Surveillance using Social Media: How URLs are shared among Reddit members

Nearly 100 people per day die from opioid overdose in the United States. Further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths. However, with increasing use of social media comes increasing opportunity to seek and share information. For instance, 80% of Internet users obtain health information online, including popular social interaction sites like Reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153.

January 21, 2018

A Data Mining Approach to Identify Climatic Determinants of Dengue Fever Patterns in French Guiana

Epidemic dynamics of dengue fever are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors [1]. The development of new methods to identify such specific characteristics becomes crucial to better understand and control spatiotemporal transmission. We concentrated our efforts on applying sequential pattern mining [2] to an epidemiological and meteorological dataset to identify potential drivers of dengue fever outbreaks.

Objective

August 22, 2018

Predicting Levels of Influenza Incidence

Influenza epidemics occur seasonally but with spatiotemporal variations in peak incidence. Many modeling studies examine transmission dynamics [1], but relatively few have examined spatiotemporal prediction of future outbreaks [2]. Bootsma et al [3] examined past influenza epidemics and found that the timing of public health interventions strongly affected the morbidity and mortality. Being able to predict when and where high influenza incidence levels will occur before they happen would provide additional lead time for public health professionals to plan mitigation strategies.

May 02, 2019

Extraction of Disease Occurrence Patterns Using MiSTIC: Salmonellosis in Florida

Infectious diseases, though initially tend to be limited geographically to a reservoir; a subsequent spatial variation in disease prevalence (including spread & intensity) arises from the underlying differences in physical-biological conditions that support pathogen, its vectors & reservoirs. Different factors like spatial proximity, physical & social connectivity, & local environmental conditions which add to its susceptibility influence the occurrence[2].

May 17, 2018

Mining Intensive Care Vitals for Leading Indicators of Adverse Health Events

The status of each Intensive Care Unit (ICU) patient is routinely monitored and a number of vital signs are recorded at sub-second frequencies which results in large amounts of data. We propose an approach to transform this stream of raw vital measurements into a sparse sequence of discrete events. Each such event represents significant departure of an observed vital sequence from the null distribution learned from reference data. Any substantial departure may be indicative of an upcoming adverse health episode.

May 02, 2019

Where are the data? Accuracy of automated EHR reporting

Over 300 independent practices transmit monthly quality reports to a data warehouse using an automated process to summarize patient information into quality measures. All practices have implemented an EHR that captures clinical information to be aggregated for population reporting, and is designed to assist providers by generating point-of-care reminders and simplify ordering and documentation.

Objective

Comparison of automated EHR-derived data with manually abstracted patient information on smoking status and cessation intervention.

June 27, 2019

Mining Pattern Model of Influenza Surveillance

This paper presents an investigation using data mining techniques to model patterns of influenza from positive case demographics, symptoms and laboratory tests.

July 30, 2018

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