A Machine Learning Approach to Improving Dynamic Decision Making

Published Online:https://doi.org/10.1287/isre.2014.0513

References

  • ACCORD Study Group (2008) Effects of intensive glucose lowering in type 2 diabetes. New England J. Medicine 358(24):2545–2559.CrossrefGoogle Scholar
  • American Diabetes Association (ADA) (2013) Economic costs of diabetes in the U.S. in 2012. Diabetes Care 36(4):1033–1046.CrossrefGoogle Scholar
  • Avins AL (2010) When clinical practice guidelines meet the black box. Arch. Internal Medicine 170(12):1013–1014.CrossrefGoogle Scholar
  • Axsäter S (2006) Inventory Control (Springer Science+Business Media, New York).Google Scholar
  • Bainbridge L (1981) Mathematical equations or processing routines? Rasmussen J, Rouse WB, eds. Human Detection and Diagnosis of Systems Failures (Plenum, New York), 259–286.CrossrefGoogle Scholar
  • Beach LR, Mitchell TR (1978) A contingency model for the selection of decision strategies. Acad. Management Rev. 3(3):439–449.CrossrefGoogle Scholar
  • Bell CM, Urbach DR, Ray JG, Bayoumi A, Rosen AB, Greenberg D, Neumann PJ (2006) Bias in published cost effectiveness studies: systematic review. British Medical J. 332:699–703.CrossrefGoogle Scholar
  • Brehmer B (1990) Strategies in real-time, dynamic decision making. Insights in Decision Making (University of Chicago Press, Chicago).Google Scholar
  • Brehmer B (1992) Dynamic decision making: Human control of complex systems. Acta Psychologica 81:211–241.CrossrefGoogle Scholar
  • Broadbent DE, FitzGerald P, Broadbent MHP (1986) Implicit and explicit knowledge in the control of complex systems. British J. Psych. 77(1):33–50.CrossrefGoogle Scholar
  • Centers for Disease Control and Prevention (CDC) (2011) National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States. U.S. Department of Health and Human Services, Atlanta, http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdfGoogle Scholar
  • Chapman RH, Stone PW, Sandberg EA, Bell C, Neumann PJ (2000) Comprehensive league table of cost-utility ratios and a sub-table of “panel-worthy” studies. Medical Decision Making 20:451–458.CrossrefGoogle Scholar
  • Conant RC, Ashby WR (1970) Every good regulator of a system must be a model of that system. Internat. J. Systems Sci. 1(2):89–97.CrossrefGoogle Scholar
  • Dutta P, Biltz GR, Johnson PE, Sperl-Hillen JM, Rush WA, Duncan JE, O'Connor PJ (2005) Simcare: A model for studying physician decision making activity. Advances in Patient Safety: From Research to Implementation (Agency for Healthcare Research and Quality, Rockville, MD), 179–192.Google Scholar
  • Edwards W (1962) Dynamic decision theory and probabilistic information processing. Human Factors 4(2):59–73.CrossrefGoogle Scholar
  • Forrester J (1971) Counterintuitive behavior of social systems. Tech. Rev. 73(3):52–68.Google Scholar
  • Friedman JH (2001) The role of statistics in the data revolution? Internat. Statist. Rev. 69(1):5–10.CrossrefGoogle Scholar
  • Funke J (1991) Solving complex problems: Exploration and control of complex systems. Sternberg R, Frensch P, eds. Complex Problem Solving—Principles and Mechanisms (Lawrence Erlbaum Associates, Hillsdale, NJ), 185–222.Google Scholar
  • Gold MR, Siegel JE, Russell LB, Weinstein MC (1996) Cost-Effectiveness in Health and Medicine (Oxford University Press, New York).CrossrefGoogle Scholar
  • Lovett MC, Anderson JR (1996) History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psych. 31:168–217.CrossrefGoogle Scholar
  • Mackinnon AJ, Wearing AJ (1985) Systems analysis and dynamic decision making. Acta Psychologica 58:159–172.CrossrefGoogle Scholar
  • Maxwell JC (1868) On governors. Proc. Royal Soc. London, Vol. 16, 270–283.Google Scholar
  • Mazze RS, Strock E, Simonson GD, Bergenstal RM (2005) Staged Diabetes Management: Prevention, Detection and Treatment of Diabetes in Adults Quick Guide (Matrex, Minneapolis).Google Scholar
  • McCabe RM (2012) Validating a computational model of patient illness: The Simcare patient model. Ph.D. thesis, University of Minnesota, Minneapolis.Google Scholar
  • McCabe RM, Johnson PE, O'Connor PJ, Sperl-Hillen J, Biltz G, Rush WA, Dutta A (2010) Validation of the SimCare model: A computational model of individual patients with type 2 diabetes. Diabetes 59(suppl 1):A353.Google Scholar
  • Nocedal J, Wright SJ (2006) Numerical Optimization, 2nd ed. (Springer, New York).Google Scholar
  • O'Brien JA, Patrick AR, Caro J (2003) Estimates of direct medical costs for microvascular and macrovascular complications resulting from type 2 diabetes mellitus in the United States in 2000. Clinical Therapeutics 25(3):1017–1038.CrossrefGoogle Scholar
  • O'Brien JA, Shomphe LA, Kavanagh PL, Raggio G, Caro JJ (1998) Direct medical costs of complications resulting from type 2 diabetes in the U.S. Diabetes Care 21(7):1122–1128.CrossrefGoogle Scholar
  • O'Connor PJ, Crain AL, Rush WA, Sperl-Hillen JM, Gutenkauf JJ, Duncan JE (2005) Impact of an electronic medical record on diabetes quality of care. Ann. Family Medicine 3:300–306.CrossrefGoogle Scholar
  • Parchman ML, Pugh JA, Romero RL, Bowers KW (2007) Competing demands or clinical inertia: The case of elevated glycosylated hemoglobin. Ann. Family Medicine 5(3):196–201.CrossrefGoogle Scholar
  • Physicians Desk Reference (PDR) (2011) PDR: Physicians Desk Reference, 66 ed. (PDR Network, Montvale, NJ).Google Scholar
  • Petitti DB (2000) Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis (Oxford University Press, New York).Google Scholar
  • Phillips L, Branch W Jr, Cook C, Doyle J, El-Kebbi I, Gallina D, Miller C, Ziemer D, Barnes C (2001) Clinical inertia. Ann. Intern. Med. 135:825–834.CrossrefGoogle Scholar
  • Quinlan JR (1987) Generating production rules from decision trees. McDermott JP, ed. Proc. Tenth Internat. Joint Conf. Artificial Intelligence (Morgan Kaufmann, Los Altos, CA), 304–307.Google Scholar
  • Quinlan JR (1993) C4.5: Programs for Machine Learning (Morgan Kaufmann, San Mateo, CA).Google Scholar
  • Ramsey G, Johnson PE, O'Connor PJ, Sperl-Hillen JM, Rush WA (2010) Using functional data analysis to identify physician decision strategies which lead to better type 2 diabetes patient outcomes. Veinot T, ed. 1st ACM Internat. Conf. Health Informatics (ACM, New York).CrossrefGoogle Scholar
  • Rapoport A (1975) Research paradigms for the study of dynamic decision behavior. Wendt D, Vlek C, eds. Utility, Probability and Human Decision Making (Reidel, Dortrecht, The Netherlands), 349–369.CrossrefGoogle Scholar
  • Reitman W (1965) Cognition and Thought: An Information-Processing Approach (John Wiley & Sons, New York).Google Scholar
  • Rochlin GL (1997) Expert operations and critical tasks. Trapped in the Net: The Unanticipated Consequences of Computerization (Princeton University Press, Princeton, NJ).Google Scholar
  • Sadoyan H, Zakarian A, Mohanty P (2004) Data mining algorithm for manufacturing process control. Internat. J. Advanced Manufacturing Tech. 28:342–350.CrossrefGoogle Scholar
  • Seborg D, Edgar TF, Mellichamp D (2004) Process Dynamics and Control (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Seborg DE, Edgar TF, Shah SL (1986) Adaptive control strategies for process control: A survey. Amer. Inst. Chemical Engineers J. 32(6):881–913.CrossrefGoogle Scholar
  • Selby J, Scanlon D, Lafata J, Villagra V, Beich J, Salber P (2003) Determining the value of disease management programs. Joint Commission J. Quality Patient Safety 29(9):491–499.CrossrefGoogle Scholar
  • Shah B, Hux J, Laupacis A, Zinman B, van Walraven C (2005) Clinical inertia in response to inadequate glycemic control. Diabetes Care 28(3):600–606.CrossrefGoogle Scholar
  • Simon HA (1969) The Sciences of the Artificial (MIT Press, Cambridge, MA).Google Scholar
  • Simon HA (1973) The structure of ill-structured problems. Artificial Intelligence 4:181–201.CrossrefGoogle Scholar
  • Sterman JD (1989) Misperceptions of feedback in dynamic decision making. Organ. Behav. Human Decision Processes 43:301–335.CrossrefGoogle Scholar
  • Stevens RJ, Kothari V, Adler AI, Stratton IM, Holman RR (2001) The UKPDS risk engine: A model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clinical Sci. 101:671–679.CrossrefGoogle Scholar
  • Sutton RS, Barto AG (1998) Reinforcement Learning: An Introduction (MIT Press, Cambridge, MA).Google Scholar
  • Tan P-N, Steinbach M, Kumar V (2006) Introduction to Data Mining (Addison-Wesley, Essex, UK).Google Scholar
  • van der Aalst WMP, Weijters AJMM (2004) Process mining: A research agenda. Comput. Indust. 53(3):231–244.CrossrefGoogle Scholar
  • Zhao H (2007) A multi-objective genetic programming approach to developing Pareto optimal decision trees. Decision Support Systems 43(3):809–826.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.