A machine-learning algorithm to identify hepatitis C in health insurance claims data

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Monitoring the burden of chronic HCV infection requires robust methods to identify patients with infection. Insurance claims data are a potentially rich source of information about disease burden, but often lack the laboratory results necessary to define chronic HCV infection.

June 18, 2019

Comparing Cerebral Palsy Surveillance Definition to ICD Codes and Written Diagnoses

Cerebral Palsy (CP) is the most common cause of motor disability in children. CP registries often rely on administrative data such as CP diagnoses or International Classification of Diseases (ICD) codes indicative of CP. However, little is known about the validity of these indicators. We calculated sensitivity, specificity, positive and negative predictive values of CP ICD-9 codes and CP diagnoses compared to a gold standard CP classification based on detailed medical and education record review.

June 18, 2019

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

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

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National Syndromic
Surveillance Program

Email:nssp@cdc.gov

The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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