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.
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.
In general, data from public health surveillance can be used for short- and long-term planning and response through retrospective data analysis of trends over time or specific events. Combining health outcome data (e.g., hospitalizations or deaths) with environmental and socio-demographic information also provides a more complete picture of most vulnerable populations. Using syndromic surveillance systems for climate and health surveillance offers the unique opportunity to help quantify and track in near-real time the burden of disease from climate and weather impacts.