Analytic Fusion for Essential Indicators of the Opioid Epidemic

Unlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis.

June 18, 2019

Supervised Learning for Automated Infectious-Disease-Outbreak Detection

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 of which. This expert knowledge represents, in the language of machine leaning, a "ground truth" for the algorithmic task of detecting outbreaks from a stream of surveillance data.

June 18, 2019

Performance of machine learning method to classify free-text medical causes of death

Mortality is an indicator of the severity of the impact of an event on the population. In France mortality surveillance is part of the syndromic surveillance system SurSaUD and is carried out by Santé publique France, the French public health agency. The set-up of an Electronic Death Registration System (EDRS) in 2007 enabled to receive in real-time medical causes of death in free-text format. This data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death (1).

June 18, 2019

Development of Text-Based Algorithm for Opioid Overdose Identification in EMS Data

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition.

June 18, 2019

Camera-based, mobile disease surveillance using Convolutional Neural Networks

Infectious diseases present with multifarious factors requiring several efforts to detect, prevent, and break the chain of transmission. Recently, machine learning has shown to be promising for automated surveillance leading to rapid and early interventions, and extraction of phenotypic features of human faces. In addition, mobile devices have become a promising tool to provide on-the-ground surveillance, especially in remote areas and geolocation mapping.

June 18, 2019

A strategy of analysis of free-text E-death certificates using machine learning

In 2004, Sante publique France, the French Public Health Agency set up a reactive all-cause mortality surveillance based on the administrative part of the death certificate, in the final objectives 1/ to detect unexpected or usual variations in mortality and 2/ to provide a first evaluation of mortality impact of events. In 2007, an Electronic Death Registration System (EDRS) was implemented, enabling electronic transmission of the medical causes of death to the agency in real-time. To date, 12% of the mortality is registered electronically.

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

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

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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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