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Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs

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Abstract

Objective

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies.

Methods

The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience.

Results

The paper provides a forward view of the impact of artificial intelligence on our human–computer support and research methods in health and life science research.

Conclusions

By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

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Adapted from expert systems: The next challenge for managers, Luconi et al. [11]

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Author information

Authors and Affiliations

  1. Auburn University, Auburn, AL, USA

    Thomas Marshall

  2. School of Biomedical Informatics, Houston, TX, USA

    Tiffiany Champagne-Langabeer

  3. University of Texas, Austin, TX, USA

    Darla Castelli

  4. The University of Texas Health Science Center at Houston, School of Public Health, Austin, TX, USA

    Deanna Hoelscher

Authors
  1. Thomas Marshall
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  2. Tiffiany Champagne-Langabeer
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  3. Darla Castelli
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  4. Deanna Hoelscher
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Correspondence to Thomas Marshall.

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Marshall, T., Champagne-Langabeer, T., Castelli, D. et al. Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs. Health Inf Sci Syst 5, 13 (2017). https://doi.org/10.1007/s13755-017-0030-0

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