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Public Health Surveillance

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A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

PloS one
OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speedi...

Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.

International journal of medical informatics
BACKGROUND: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveil...

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults.

Nutrients
The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically fr...

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.

International journal of environmental research and public health
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunizat...

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tw...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

Surgery
BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these...

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

JAMA network open
IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.

Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

Journal of medical Internet research
BACKGROUND: During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have pos...

Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare.

IEEE/ACM transactions on computational biology and bioinformatics
Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it i...