Literature DB >> 30760118

Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.

Jason H Moore1, Mary Regina Boland1, Pablo G Camara1, Hannah Chervitz1, Graciela Gonzalez1, Blanca E Himes1, Dokyoon Kim1, Danielle L Mowery1, Marylyn D Ritchie1, Li Shen1, Ryan J Urbanowicz1, John H Holmes1.   

Abstract

Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.

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Mesh:

Year:  2019        PMID: 30760118      PMCID: PMC7545355          DOI: 10.2217/pme-2018-0145

Source DB:  PubMed          Journal:  Per Med        ISSN: 1741-0541            Impact factor:   2.512


  49 in total

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Review 3.  Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.

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7.  scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.

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  7 in total

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