The 2nd Edition: Big Data for Public Health Research and Practice
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 5176
Special Issue Editor
Interests: neighborhoods; health disparities; social media; Google Street View; health technology; artificial intelligence; social epidemiology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
To enable decision making, we need timely data on the determinants of health and well-being. Big data can often be operational or “organic” data generated not for research purposes, including social media; news feeds; Google Street View images; online reviews; blogs; and billing, pharmacy, and laboratory data. These data are providing new ways of obtaining information on factors such as social norms, built environment features, health behaviors, and individual characteristics that can impact health.
After the success of the previous Special Issue on “Big Data for Public Health Research and Practice”, we are pleased to invite researchers to contribute to the second Special Issue. Similarly, the aim of this Special Issue is to collect a series of articles related to big data and the development of technologies and advanced computational methods that can be leveraged to conduct public health research and practice. Practical experiences and experiments are also welcome.
Some possible topics are listed below; however, other topics are also welcomed:
- The use of electronic health records, billing data, and pharmacy data to understand individualized risk factors and treatment success;
- The characterization of built environments with big data derived from various sources (e.g., Google Street View images and remote sensing imagery data), as well as their impacts on people’s health;
- The use of Artificial Intelligence to build conversational or question-and-answer chatbots for personalized health information delivery;
- Using various forms of user-generated content (e.g., GPS data, accelerometer data, users’ review data, social media data, and web search data) to study individual behaviors and social/cultural environments, as well as their impacts on people’s health;
- The development of new methods or tools (e.g., natural language processing, machine learning, database management, high performance computing, data mining, cloud computing, computer vision, visualization, geographic information systems, and spatial analysis) for big-data-based health research;
- The use of big data in COVID-19-related research;
- The application or development of causal inference methods for big data;
- Investigating and addressing data quality and uncertainty issues;
- The blending and integration of big data from different sources.
Dr. Quynh C. Nguyen
Guest Editor
Manuscript Submission Information
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Keywords
- big data
- natural language processing
- machine learning
- deep learning
- artificial intelligence
- environment
- public health
- health interventions using technology
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