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Population Surveillance

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Visualization of Cancer and Cardiovascular Disease Co-Occurrence With Network Methods.

JCO clinical cancer informatics
PURPOSE: Cancer and cardiovascular disease (CVD) are common causes of morbidity and mortality, and measurement and interpretation of their co-occurrence rate have important implications for public health and patient care. Here, we present the raw and...

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...

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...

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 ...

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...

Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

JCO clinical cancer informatics
PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate population-based research in molecularly defined subgroups of non-small-cell lung cancer (NSCLC), we...

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...

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...

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Journal of the National Cancer Institute
BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid applicati...

Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.