16,635 research outputs found

    Evaluating health effects of transport interventions: methodologic case study

    Get PDF
    Background: There is little evidence about the effects of environmental interventions on population levels of physical activity. Major transport projects may promote or discourage physical activity in the form of walking and cycling, but researching the health effects of such “natural experiments” in transport policy or infrastructure is challenging. Methods: Case study of attempts in 2004–2005 to evaluate the effects of two major transport projects in Scotland: an urban congestion charging scheme in Edinburgh, and a new urban motorway (freeway) in Glasgow. Results: These interventions are typical of many major transport projects. They are unique to their context. They cannot easily be separated from the other components of the wider policies within which they occur. When, where, and how they are implemented are political decisions over which researchers have no control. Baseline data collection required for longitudinal studies may need to be planned before the intervention is certain to take place. There is no simple way of defining a population or area exposed to the intervention or of defining control groups. Changes in quantitative measures of health-related behavior may be difficult to detect. Conclusions: Major transport projects have clear potential to influence population health, but it is difficult to define the interventions, categorize exposure, or measure outcomes in ways that are likely to be seen as credible in the field of public health intervention research. A final study design is proposed in which multiple methods and spatial levels of analysis are combined in a longitudinal quasi-experimental study

    Technology Directions for the 21st Century

    Get PDF
    The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter

    Advanced sensors technology survey

    Get PDF
    This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

    Linking social media, medical literature, and clinical notes using deep learning.

    Get PDF
    Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts

    Use of Real-World Data in Pharmacovigilance Signal Detection

    Get PDF

    Use of Real-World Data in Pharmacovigilance Signal Detection

    Get PDF
    corecore