Digital Epidemiology: designing machine learning approaches to combine Internet-based data sources to monitor and forecast disease activity in multiple locations and spatial resolutions

Presented May 24, 2018.

Mauricio Santillana, MS, PhD describes machine learning methodologies that leverage Internet-based information from search engines, twitter microblogs, crowd-sourced disease surveillance systems, electronic medical records, and historical synchronicities in disease activity across spatial regions, to successfully monitor and forecast disease outbreaks in multiple locations around the globe in near real-time.

Presenter

May 24, 2018

Social Network Analysis across Healthcare Entities, Orange County, FL, 2016

In the realm of public health, there has been an increasing trend in exploration of social network analyses (SNAs). SNAs are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1.

January 19, 2018

Automated Processing of Electronic Data for Disease Surveillance

National initiatives, such as Meaningful Use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. However, electronic reporting has also lead to significant increases in the number of cases reported to public health. In order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring.

January 25, 2018

Forecasting Emergency Department Admissions for Pneumonia in Tropical Singapore

Pneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections.

January 19, 2018

How can we assess the effects of urban environment on obesity using aggregated data?

Where we live' affects 'How we live'. Information about 'how one lives' collected from the public health surveillance data such as the Behavioral Risk Factor Surveillance System (BRFSS). Neighborhood environment surrounding individuals affects their health behavior or health status are influenced as well as their own traits. Meanwhile, geographical information of subjects recruited in the health behavior surveillance data is usually aggregated at administrative levels such as a county.

January 25, 2018

Free-Text Mining to Improve Syndrome Definition Matching Across Emergency Departments

Standard syndrome definitions for ED visits in ESSENCE rely on chief complaints. Visits with more words in the chief complaint field are more likely to match syndrome definitions. While using ESSENCE, we observed geographic differences in chief complaint length, apparently related to differences in electronic health record (EHR) systems, which resulted in disparate syndrome matching across Idaho regions.

January 21, 2018

How Missing Discharge Diagnosis Data in Syndromic Surveillance Leads to Coverage Gaps

Indiana utilizes the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) to collect and analyze data from participating hospital emergency departments. This real-time collection of health related data is used to identify disease clusters and unusual disease occurrences. By Administrative Code, the Indiana State Department of Health (ISDH) requires electronic submission of chief complaints from patient visits to EDs. Submission of discharge diagnosis is not required by Indiana Administrative Code, leaving coverage gaps.

January 25, 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

Machine Learning for Identifying Relevance to Biosurveillance in Multilingual Text

Global biosurveillance is an extremely important, yet challenging task. One form of global biosurveillance comes from harvesting open source online data (e.g. news, blogs, reports, RSS feeds). The information derived from this data can be used for timely detection and identification of biological threats all over the world. However, the more inclusive the data harvesting procedure is to ensure that all potentially relevant articles are collected, the more data that is irrelevant also gets harvested. This issue can become even more complex when the online data is in a non-native language.

January 25, 2018

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

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