A Syndrome Definition Validation Approach for Zika Virus

In 2016, the World Health Organization declared Zika virus a global public health emergency. Zika infection during pregnancy can cause microcephaly and other fetal brain defects. To facilitate clinicians’ ability to detect Zika, various syndrome definitions have been developed. 


To develop and validate a Zika virus disease syndrome definition within the GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification) surveillance system.

May 26, 2017

Relationship Between Baseline Influenza-like Illness Rates And Healthcare Settings

The primary goal of syndromic surveillance is early recognition of disease trends, in order to identify and control infectious disease outbreaks, such as influenza. For surveillance of influenza-like illness (ILI), public health departments receive data from multiple sources with varying degrees of patient acuity, including outpatient clinics and emergency departments. However, the lack of standardization of these data sources may lead to varying baseline levels of ILI activity within a local area.


August 08, 2017

Utility of Natural Language Processing for Clinical Quality Measures Reporting

Clinical quality measures (CQMs) are tools that help measure and track the quality of health care services. Measuring and reporting CQMs helps to ensure that our health care system is delivering effective, safe, efficient, patient-centered, equitable, and timely care. The CQM for influenza immunization measures the percentage of patients aged 6 months and older seen for a visit between October 1 and March 31 who received (or reports previous receipt of) an influenza immunization.

August 26, 2017

Natural Language Processing and Technical Challenges of Influenza-Like Illness Surveillance

Processing free-text clinical information in an electronic medical record (EMR) may enhance surveillance systems for early identification of ILI outbreaks. However, processing clinical text using NLP poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems.


September 01, 2017

The Impact of Documentation Style on Influenza-Like Illness Rates in the Emergency Department

In recent years, the threat of pandemic influenza has drawn extensive attention to the development and implementation of syndromic surveillance systems for early detection of ILI. Emergency department (ED) data are key components for syndromic surveillance systems. However, the lack of standardization for the content in chief complaint (CC) free-text fields may make it challenging to use these elements in syndromic surveillance systems. Furthermore, little is known regarding how ED data sources should be structured or combined to increase sensitivity without elevating false positives.

September 21, 2017

Which Sections of Electronic Medical Records Are Most Relevant for Real-Time Surveillance of Influenza- like Illness?

Effective real-time surveillance of infectious diseases must strike a balance between reliability and timeliness for early detection. Traditional syndromic surveillance utilizes limited sections of the EMR, such as chief complaints and/or diagnosis. However, other sections of the EMR may contain more pertinent information than what is captured in a brief chief complaint. These other EMR sections may provide relevant information earlier in the patient encounter than at the diagnosis or disposition stage, which can appear in the EMR up to 24 hours after the patient’s discharge.

September 28, 2017

Creating a Local Geographic Influenza-like Illness Activity Report

Mapping ILI surveillance data can be useful in identifying the direction and speed of an outbreak and for focusing control measures for an efficient public health response. The Centers for Disease Control and Prevention’s (CDC) ILINet currently displays weekly ILI geographic data at a national/regional/state level, but this visual data could also be useful at the local level.


To create a local geographic influenza-like illness (ILI) activity report.

October 03, 2017

A Syndrome Definition Validation Approach for Ebola Virus Disease

The 2014 Ebola outbreak in West Africa is one of the largest Ebola outbreaks in history. Early detection is critical for rapid initiation of treatment, infection control and emergency response plans. To facilitate clinicians’ ability to detect Ebola, various syndrome definitions have been developed.


To develop and validate an Ebola virus disease syndrome definition within the GUARDIAN (Geographic Utilization of Artificial Intelligence in Real-Time for Disease Identification and Alert Notification) surveillance system.

October 23, 2017

The Impact of Weather on Influenza-like Illness Rates in Chicago

Weather events such as a heat wave or a cold snap can cause a change to the number of patients and types of symptoms seen at a healthcare facility. Understanding the impact of weather patterns on ILI surveillance may be useful for early detection and trend analysis. In addition, weather patterns limit our ability to extrapolate data collected in one region to a different region, which may not share the same weather or periodic trends. By modeling these sources of variation, we can factor out their effects and increase the sensitivity of our overall surveillance system.

December 11, 2017

Technical Challenges of Syndromic Surveillance System Deployment in a Health Information Exchange

Adoption of electronic medical records is on the rise, due to the Health Information Technology for Economic and Clinical Health Act and meaningful use incentives. Simultaneously, numerous HIE initiatives provide data sharing flexibility to streamline clinical care. Due to the consolidated data availability in centralized HIE models, conducting syndromic surveillance using locally developed systems, such as GUARDIAN, is becoming feasible. During the past year, Chicago has embarked on a city-wide HIE deployment campaign.

May 02, 2019


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