Within the traditional surveillance of notifiable infectious diseases in Germany, not only are individual cases reported to the Robert Koch Institute, but also outbreaks themselves are recorded: A label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and... Read more
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This presentation given August 3, 2017 describes work toward applying machine learning methods to CDC’s autism surveillance program. CDC’s population-based autism surveillance is labor-intensive and costly, as it requires clinicians to manually review children’s medical and educational records... Read more
Content type: Webinar
Presented January 26, 2017.
This presentation will describe the steps involved in machine learning and will include a demo an application to detect carbon monoxide poisoning in the Kansas syndromic surveillance data.
Content type: Webinar
At the Governorâs Opioid Addiction Crisis Datathon in September 2017, a team of Booz Allen data scientists participated in a two-day hackathon to develop a prototype surveillance system for business users to locate areas of high risk across multiple indicators in the State of Virginia. We... Read more
Content type: Abstract
A socio-marker is a measurable indicator of social conditions where a patient is embedded in and exposed to, being analogous with a biomarker indicating the severity or presence of some disease state. Social factors are one of the most clinical health determinants, which play a critical role in... Read more
Content type: Abstract
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... Read more
Content type: Abstract
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... Read more
Content type: Webinar
Presented November 16, 2018.
The current opioid overdose/addiction crisis in the United States presents a challenge to public health intervention due to a lack of data on current and past incidence. Very little information is known regarding what is happening when/where and in comparison... Read more
Content type: Webinar
Presented November 27, 2018.
Unstructured data such as chief complaints and provider notes are an important component of effective Health surveillance. Applying machine learning (ML) and natural language processing (NLP) to unstructured data can often improve surveillance performance over... Read more
Content type: Webinar
Presented December 13, 2018.
For public health surveillance, is machine learning worth the effort? What methods are relevant? Do you need special hardware? This talk was motivated by these and other questions asked by ISDS members. It will focus on providing practical—and slightly... Read more
Content type: Webinar
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