Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs
- 221 Downloads
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.
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.
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.
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.
KeywordsCognitive computing Artificial intelligence Obesity intervention
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 2.Hey T, Tansley S, Tolle KM, editors. The fourth paradigm: data intensive scientific discovery. Jim Grey, foreword, XVII, Microsoft Research; 2009.Google Scholar
- 3.Das R. Five technologies that will disrupt healthcare by 2020. Forbes.com, 30 Mar 2016.Google Scholar
- 4.Response to—request for information preparing for the future of artificial intelligence. Preparing for the future of artificial intelligence. http://research.ibm.com/cognitive-computing/ostp/rfi-response.shtml.
- 5.IBM. Cleveland clinic, IBM continue their collaboration to establish model for cognitive population health management and data-driven personalized healthcare, News release, Cleveland, OH and Armonk, NY. http://www-03.ibm.com/press/us/en/pressrelease/51290.wss. Accessed 22 Dec 2016
- 6.Garrison LP Jr. Universal health coverage—big thinking versus big data. J Int Soc Pharmaconomics Res. 2013;16(1):S1–3.Google Scholar
- 11.Luconi FL, Malone TW, Scott-morton MS. expert systems: the next challenge for managers. Sloan Manag Rev. Summer. 1986;27(4):3.Google Scholar
- 12.Oinas-Kukkonen H, Harjumaa M. Persuasive systems design: key issues, process model, and system features. Commun Assoc Inf Syst. 2009;24:28.Google Scholar
- 18.Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. IEEE. 2009;24(2):8–12.Google Scholar
- 19.Klauser F, Albrechtslund A. From self-tracking to smart urban infrastructures: towards an interdisciplinary research agenda on Big Data. Surveill Soc. 2014;12(2):273–86.Google Scholar
- 21.Newell A. Reasoning: problem solving and decision processes: the problem space as a fundamental category. In: Nickerson R, editor. Attention and performance VIII. Hillsdale: Erlbaum; 1980.Google Scholar
- 22.Newell A, Simon HA. Human problem solving. Englewood Cliffs: Prentice-Hall; 1972.Google Scholar
- 25.Berman JJ. Principles of big data: preparing, sharing and analyzing complex information. Amsterdam: Elsevier, Inc; 2013.Google Scholar
- 27.McGuire S. Accelerating progress in obesity prevention: solving the weight of the nation, Advanced Nutrition 2012 1:3 (5) 7808-709. Institute of Medicine (IOM). Washington, DC: The National Academies Press; 2012.Google Scholar
- 29.Hekler EB, Buman MP, Poothakandiyil N, Rivera DE, Dzierzewski JM, Morgan AA, et al. Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention. Health Educ Behav. 2013;40(10):51S–62S.CrossRefPubMedGoogle Scholar
- 31.Timms KP, Rivera DE, Piper ME, Collins LM. A hybrid model predictive control strategy for optimizing a smoking cessation intervention. In: Proceedings of the 2014 American control conference; 2014. p. 2389–2394.Google Scholar
- 34.Roberts JP, Fisher TR, Trowbridge MJ, Bent C. A design thinking framework for healthcare management and innovation. Healthc J Deliv Sci Innov. 2016;4(1):11–4.Google Scholar