Closing the Knowledge Gap
Uncovering hidden knowledge in scientific literature with machine learning.
The rapid pace of publication for scientific papers has a dark side: data overload. This knowledge lag impacts healthcare practitioners, researchers, policymakers and educators.
Natural language query allows researchers to ask simple questions using normal language. This allows researchers and practitioners to query the knowledge base directly, without any programming experience.
Machine learning reduces the time and effort required to review scientific literature, identify relevant citations for a specific research question and identify hidden connections between different data sets.
Battelle and the Centers for Medicare & Medicaid Services (CMS) worked to reduce the time it takes to locate and evaluate the most relevant and usable articles for measure development.
The pace of publication for new biomedical research makes it difficult, if not impossible, for researchers and practitioners to keep up with new discoveries, even with a narrowly defined field. These human limitations add to the lag time between published research and changes in clinical practice. They also make it hard to find hidden connections within the knowledge base that could lead to new insights and guide the direction of future research.
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