Abstract
The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One’s aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.
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References
Institute for Health Technology Transformation (2013) Transforming health care through big data – strategies for leveraging big data in the health care industry. Institute for Health Technology Transformation Edition
Wang Y, Kung LA, Terry Anthony Byrd TA (2018) Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change 126:3–13
Archenaa J, Mary-Anita EA (2015) A survey of big data analytics in healthcare and government. Proc Comput Sci 50:408–413
Wang Y, Kung LA, Wang WY, Cegielski CG (2018) An integrated big data analytics-enabled transformation model: application to health care. Information & Management 55:67–79
Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Pearson, Harlow, UK
Holland J (1962) Outline for logical theory of adaptative systems. J ACM 9:297–314
Fogel L, Owens A, Walsh M (1966) Artificial intelligence through a simulated evolution. John Wiley & Sons
Koza J (1992) Genetic programming. MIT Press, Cambridge, USA
Jong K (2006) Evolutionary computation a unified approach. MIT Institute for Health Technology Transformation Press, Cambridge, USA
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2:1–21
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536
Nuti S, Vainieri M (2012) Managing waiting times in diagnostic medical imaging. BMJ Open 2:e001255
McEnery KW (2013) Radiology information systems and electronic medical records. In IT Reference Guide for the Practicing Radiologist, pp. 1–14, American College of Radiology, USA
Fotiadou A (2013) Choosing and Visualizing Waiting Time Indicators in Diagnostic Medical Imaging Department for Different Purposes and Audiences. Master’s Thesis in Health Informatics, Karolinska Institutet, Sweden
Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson Education, New Jersey, USA
Fernandes F, Vicente H, Abelha A, Machado J, Novais P, Neves J (2015) Artificial neural networks in diabetes control. Proc 2015 Sci Info Conf (SAI 2015): 362–370, IEEE Ed
Silva A, Vicente H, Abelha A, Santos MF, Machado J, Neves J, Neves J (2016) Length of stay in intensive care units – a case base evaluation. In: Fujita H, Papadopoulos GA (eds) New trends in software methodologies, tools and techniques, frontiers in artificial intelligence and applications, vol 286. IOS Press, Amsterdam, pp 191–202
Kakas A, Kowalski R, Toni F (1998) The role of abduction in logic programming. In: Gabbay D, Hogger C, Robinson I (eds) Handbook of logic in artificial intelligence and logic programming, vol 5. Oxford University Press, Oxford, pp 235–324
Neves J (1984) A logic interpreter to handle time and negation in logic databases. In: Muller R, Pottmyer J (eds) Proceedings of the 1984 annual conference of the ACM on the 5th generation challenge. Association for Computing Machinery, New York, pp 50–54
O’Neil P, O’Neil B, Chen X (2009) Star schema benchmark. Revision 3, June 5, http://www.cs.umb.edu/~poneil/StarSchemaB.pdf, last accessed 2018/01/23
Vicente H, Dias S, Fernandes A, Abelha A, Machado J, Neves J (2012) Prediction of the quality of public water supply using artificial neural networks. Journal of Water Supply: Research and Technology – AQUA 61:446–459
Vicente H, Couto C, Machado J, Abelha A, Neves J (2012) Prediction of water quality parameters in a reservoir using artificial neural networks. Int J Design Nat Ecodynam 7:309–318
Vicente H, Roseiro J, Arteiro J, Neves J, Caldeira AT (2013) Prediction of bioactive compound activity against wood contaminant fungi using artificial neural networks. Can J For Res 43:985–992
Florkowski CM (2008) Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin Biochem Rev 29(Suppl 1):S83–S87
Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29:31–44
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Neves, J., Vicente, H., Esteves, M. et al. A Deep-Big Data Approach to Health Care in the AI Age. Mobile Netw Appl 23, 1123–1128 (2018). https://doi.org/10.1007/s11036-018-1071-6
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DOI: https://doi.org/10.1007/s11036-018-1071-6