Thomas Marshall1, Tiffiany Champagne-Langabeer2, Darla Castelli3, Deanna Hoelscher4. 1. Auburn University, Auburn, AL USA. 2. School of Biomedical Informatics, Houston, TX USA. 3. University of Texas, Austin, TX USA. 4. The University of Texas Health Science Center at Houston, School of Public Health, Austin, TX USA.
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.
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.
Authors: Eric B Hekler; Matthew P Buman; Nikhil Poothakandiyil; Daniel E Rivera; Joseph M Dzierzewski; Adrienne Aiken Morgan; Christina S McCrae; Beverly L Roberts; Michael Marsiske; Peter R Giacobbi Journal: Health Educ Behav Date: 2013-10