Human-learned lessons about machine learning in public health surveillance

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 opinionated—advice about how to determine whether machine learning could be a useful tool for your problem.

Presenter

December 21, 2018

Opioid Overdose Surveillance and Classification with R

Presented November 16, 2018.

November 30, 2018

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

Developing a Prototype Opioid Surveillance System at a 2-Day Virginia Hackathon

At the Governor’s 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 addressed 1) how different geographic regions experience the opioid overdose epidemic differently by clustering similar counties by socieconomic indicators, and 2) facilitating better data sharing between health care providers and law enforcement.

January 25, 2018

Identifying Sociomarkers of Pediatric Asthma Patients at Risk of Hospital Revisiting

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 explaining health outcomes. Socio-markers can help medical practitioners and researchers to reliably identify high-risk individuals in a timely manner.

Objective:

January 25, 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

Rapid classification of autism for public health surveillance

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 for descriptions of autism symptoms. Using the words in these records, our team is building algorithms to predict which children will meet the surveillance case definition for autism. This talk describes our early results, recent progress, and perspectives gained from working with textual data.

August 04, 2017

Machine Learning in R: Detecting Carbon Monoxide Poisoning in Syndromic Surveillance Data

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.

September 21, 2017

Analytics, Machine Learning & NLP -- use in BioSurveillance and Public Health practice

Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today’s applications.

September 25, 2017

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INTERNATIONAL SOCIETY FOR
DISEASE SURVEILLANCE

288 Grove Street, Box 203
Braintree, MA 02184
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Email:syndromic@syndromic.org

This Knowledge Repository is made possible through the activities of the Centers for Disease Control and Prevention Cooperative Agreement/Grant #1 NU500E000098-01, National Surveillance Program Community of Practice (NSSP-CoP): Strengthening Health Surveillance Capabilities Nationwide, which is in the interest of public health.

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