Publications by authors named "George Meng"

Introduction: Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways.

Methods: Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements.

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This paper offers a case study to demonstrate how a complex scoring model tool called CNS-TAP, originally created by a neuro-oncology team at one institution, was upgraded and made accessible to a wider audience. In the Results and Discussion, many issues of web app design, development, and sustainability are covered. Overall, we chart a path to expand access to many unique software tools created and needed by today's medical specialists.

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A coarse classification of medications into two risk categories, one for high-risk medications and one for all others, allows people to focus safety improvement work on medications that carry the highest risks of harm. However, such coarse categorization does not distinguish the relative risk of harm for the majority of medications. To begin to develop a more fine-grained measurement scale for the relative risk of harm spanning many medications, we performed an experiment with 18 practicing pharmacists.

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Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice.

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The Knowledge Grid (KGrid) is a research and development program toward infrastructure capable of greatly decreasing latency between the publication of new biomedical knowledge and its widespread uptake into practice. KGrid comprises digital knowledge objects, an online Library to store them, and an Activator that uses them to provide Knowledge-as-a-Service (KaaS). KGrid's Activator enables computable biomedical knowledge, held in knowledge objects, to be rapidly deployed at Internet-scale in cloud computing environments for improved health.

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Throughout the world, biomedical knowledge is routinely generated and shared through primary and secondary scientific publications. However, there is too much latency between publication of knowledge and its routine use in practice. To address this latency, what is actionable in scientific publications can be encoded to make it computable.

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