Mining big data to support decision making in healthcare

References

• Adnan, M. H. M., & Husain, W. (2012). Hybrid approaches using decision tree, naive Bayes, means and Euclidean distances for childhood obesity prediction. International Journal of Software Engineering and Its Applications, 6(3), 99–106.
   Google Scholar
• Afifi, A. A., & Clark, V. (1990). Computer-aided multivariate analysis. New York, NY: Van Nostrand Reinhold Co.
   Google Scholar
• Agarwal, R., & Dhar, V. (2014). Editorial—Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448. doi:10.1287/isre.2014.0546
   Web of Science ®Google Scholar
• Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine for all: Challenges and opportunities. BMC Medical Genomics, 8(1), 33. doi:10.1186/s12920-015-0108-y
   PubMed Web of Science ®Google Scholar
• Atkinson, R. I. (2005). Etiologies of obesity. In D. J. Goldstein (Ed.), The management of eating disorders and obesity (Vol. 2). Totowa, NJ: Humana Press.
   Google Scholar
• Boone-Heinonen, J., Gordon-Larsen, P., & Adair, L. S. (2008). Obesogenic clusters: Multidimensional adolescent obesity-related behaviors in the US. Annals of Behavioral Medicine, 36(3), 217–230. doi:10.1007/s12160-008-9074-3
   PubMed Web of Science ®Google Scholar
• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (1st ed.). Belmont, CA: Wadsworth.
   Google Scholar
• Buxton, O. M., & Marcelli, E. (2010). Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Social Science & Medicine, 71(5), 1027–1036. doi:10.1016/j.socscimed.2010.05.041
   PubMed Web of Science ®Google Scholar
• Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31(1), 219–230. doi:10.1016/j.jhealeco.2011.10.003
   PubMed Web of Science ®Google Scholar
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
   Web of Science ®Google Scholar
• Chou, S. Y., Grossman, M., & Saffer, H. (2004). An economic analysis of adult obesity: Results from the Behavioral Risk Factor Surveillance System. Journal of Health Economics, 23(3), 565–587.
   PubMed Web of Science ®Google Scholar
• Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73. Popper, K. (1963). Conjectures and Refutations, Routledge, London. doi:10.1145/2534706
   Web of Science ®Google Scholar
• Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 849–911. doi:10.1111/rssb.2008.70.issue-5
   PubMed Web of Science ®Google Scholar
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases AI Magazine, 17(3), 37–54.
   Google Scholar
• Ferenci, T., Almássy, Z., Merkei, Z. O., Kovács, A., & Kovács, L. (2008). Cluster analysis of obesity-related parameters of Hungarian children. Proceedings of BUDAMED, 8, 33–37.
   Google Scholar
• Finkelstein, E. A., Ruhm, C. J., & Kosa, K. M. (2005). Economic causes and consequences of obesity. Annual Reviews Public Health, 26, 239–257. doi:10.1146/annurev.publhealth.26.021304.144628
   PubMed Web of Science ®Google Scholar
• Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. The Journal of the American Medical Association, 307(5), 491–497. doi:10.1001/jama.2012.39
   PubMed Web of Science ®Google Scholar
• Flegal, K. M., Kit, B. K., Orpana, H., & Graubard, B. (2013). Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. The Journal of the American Medical Association, 309(1), 71–82. doi:10.1001/jama.2012.113905
   PubMed Web of Science ®Google Scholar
• Flegal, K. M., Kruszon-Moran, D., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2016). Trends in obesity among adults in the United States, 2005 to 2014. The Journal of the American Medical Association, 315(21), 2284–2291.
   PubMed Web of Science ®Google Scholar
• Fryar, C. D., Carroll, M. D., & Ogden, C. L. (2012). Prevalence of overweight, obesity, and extreme obesity among adults: United States, trends 1960–1962 through 2009–2010. Hyattsville, MD: National Center for Health Statistics.
   Google Scholar
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. doi:10.1016/j.ijinfomgt.2014.10.007
   Web of Science ®Google Scholar
• George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326. doi:10.5465/amj.2014.4002
   Web of Science ®Google Scholar
• Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642.
   Web of Science ®Google Scholar
• Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’ revolution in healthcare. New York, NY: McKinsey & Company.
   Google Scholar
• Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design (Vol. 20). Boston, MA: PWS publishing company.
   Google Scholar
• Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining. Cambridge, MA: MIT press.
   Google Scholar
• Harper, P. R. (2005). A review and comparison of classification algorithms for medical decision making. Health Policy, 71(3), 315–331. doi:10.1016/j.healthpol.2004.05.002
   PubMedGoogle Scholar
• Hebert, J. R., Allison, D. B., Archer, E., Lavie, C. J., & Blair, S. N. (2013, June). Scientific decision making, policy decisions, and the obesity pandemic. Mayo Clinic Proceedings, 88, 593–604.
   Google Scholar
• Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119–127. doi:10.2307/2986296
   Google Scholar
• Kim, D. D., & Basu, A. (2016). Estimating the medical care costs of obesity in the United States: Systematic review, meta-analysis, and empirical analysis. Value in Health, 19(5), 602–613.
   PubMed Web of Science ®Google Scholar
• Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481. doi:10.1177/2053951714528481
   Google Scholar
• Kung, H. C., Hoyert, D. L., Xu, J. Q., & Murphy, S. L. (2008). Deaths: Final data for 2005: National vital statistics reports (vol. 56, no. 10). Hyattsville, MD: National Center for Health Statistics.
   Google Scholar
• Laaksonen, M., Práttalä, R., & Karisto, A. (2001). Patterns of unhealthy behaviour in Finland. The European Journal of Public Health, 11(3), 294–300. doi:10.1093/eurpub/11.3.294
   PubMed Web of Science ®Google Scholar
• Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032–2033. doi:10.14778/2367502
   Google Scholar
• Levi, J., Segal, L. M., Thomas, K., St. Laurent, R., Lang, A., & Rayburn, J. (2013). F as in fat: How obesity threatens America’s future 2013. Washington, DC: Trust for America’s Health and the Robert Wood Johnson Foundation.
   Google Scholar
• Li, Y., Lin, Z., Xu, L., & Swain, A. (2015). “Do the electronic books reinforce the dynamics of book supply chain market?”–A theoretical analysis. European Journal of Operational Research, 245(2), 591–601. doi:10.1016/j.ejor.2015.01.048
   Web of Science ®Google Scholar
• Motulsky, A. G. (1989). Diet and health: Implications for reducing chronic disease risk. Washington, DC: National Academies Press.
   Google Scholar
• Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. The Journal of the American Medical Association, 311(8), 806–814. doi:10.1001/jama.2014.732
   PubMed Web of Science ®Google Scholar
• Ogden, C. L., Yanovski, S. Z., Carroll, M. D., & Flegal, K. M. (2007). The epidemiology of obesity. Gastroenterology, 132(6), 2087–2102. doi:10.1053/j.gastro.2007.03.052
   PubMed Web of Science ®Google Scholar
• Poortinga, W. (2007). The prevalence and clustering of four major lifestyle risk factors in an English adult population. Preventive Medicine, 44(2), 124–128. doi:10.1016/j.ypmed.2006.10.006
   PubMed Web of Science ®Google Scholar
• Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. doi:10.1089/big.2013.1508
   PubMed Web of Science ®Google Scholar
• Quinlan, J. R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.), Expert Systems in the Micro-Electronic Age (pp. 168–201). Edinburgh, UK: Edinburgh University Press.
   Google Scholar
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. doi:10.1007/BF00116251
   Google Scholar
• Quinlan, J. R. (2014). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Raghupathi, W. (2016). Data mining in healthcare. In S. P. Kudyba (Ed.), Healthcare Informatics: Improving Efficiency through Technology, Analytics, and Management (pp. 353–372). Boca Raton, FL: CRC Press.
   Google Scholar
• Strine, T. W., & Chapman, D. P. (2005). Associations of frequent sleep insufficiency with health-related quality of life and health behaviors. Sleep Medicine, 6(1), 23–27. doi:10.1016/j.sleep.2004.06.003
   PubMed Web of Science ®Google Scholar
• Stunkard, A. J., & Wadden, T. A. (1993). Obesity: Theory and therapy (2nd ed.). New York, NY: Raven Press.
   Google Scholar
• Sudo, N., Degeneffe, D., Vue, H., Merkle, E., Kinsey, J., Ghosh, K., & Reicks, M. (2009). Relationship between attitudes and indicators of obesity for midlife women. Health Education & Behavior, 36, 1082–1094. doi:10.1177/1090198109335653
   PubMed Web of Science ®Google Scholar
• Swain, A. (2016). Big data analytics: An expert interview with Bipin Chadha, data scientist for United Services Automobile Association (USAA). Journal of Information Technology Case and Application Research, 18(3), 181–185.
   Google Scholar
• Swain, A., & Cao, Q. (2013, November). Exploring the impact of social media on supply chain performance: A sentiment analysis. Paper presented at the Decision Sciences Institute (DSI) annual meeting, Baltimore, MD.
   Google Scholar
• Swain, A., & Cao, Q. (2014a, January). Impact of online Firm Generated Content (FGC) on supply chain performance: An empirical analysis. Paper presented at the 47th Annual Hawaii International Conference on System Sciences, Waikoloa, HI. doi:10.1109/HICSS.2014.77
   Google Scholar
• Swain, A., & Cao, Q., (2014b, November). Strategic alignment and sustainability performance: An operations management perspective. Paper presented at the Decision Sciences Institute (DSI) annual meeting, Tampa, FL.
   Google Scholar
• Uchino, B. N., Cacioppo, J. T., & Kiecolt-Glaser, J. K. (1996). The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin, 119(3), 488. doi:10.1037/0033-2909.119.3.488
   PubMed Web of Science ®Google Scholar
• Yap, B. W., Ong, S. H., & Husain, N. H. M. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38(10), 13274–13283. doi:10.1016/j.eswa.2011.04.147
   Web of Science ®Google Scholar
• Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: A survey of the literature. Journal of Medical Systems, 36(4), 2431–2448. doi:10.1007/s10916-011-9710-5
   PubMed Web of Science ®Google Scholar
• Zurada, J., & Lonial, S. (2011). Comparison of the performance of several data mining methods for bad debt recovery in the healthcare industry. Journal of Applied Business Research, 21(2), 37–54.
   Google Scholar