AIMC Topic: Population Surveillance

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Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things.

International journal of environmental research and public health
The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environ...

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses.

Transboundary and emerging diseases
Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird popula...

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data.

Zoonoses and public health
The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniqu...

Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.

Breast cancer research : BCR
BACKGROUND: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminat...

Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

PloS one
BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-op...

Chief complaint classification with recurrent neural networks.

Journal of biomedical informatics
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Curre...

Drivers for the development of an Animal Health Surveillance Ontology (AHSO).

Preventive veterinary medicine
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields ...

Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations.

Journal of medical Internet research
BACKGROUND: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting signif...

Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study.

Clinical & experimental ophthalmology
IMPORTANCE: Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records.