Decision-theoretic refinement planning in medical decision making: Management of acute deep venous thrombosis

Medical Decision Making

Volumn 16, Issue 4, 1996, Pages 315-325

  Haddawy, Peter ^a,b   Doan, Anhai ^a   Kahn Jr , Charles E ^a,b  

^a University of Wisconsin Milwaukee   (United States)

^b MEDICAL COLLEGE OF WISCONSIN   (United States)

Author keywords

acute deep venous thrombosis; artificial intelligence; decision theoretic refinement planning; diagnosis; DRIPS; treatment

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CONTROLLED STUDY; DECISION THEORY; DEEP VEIN THROMBOSIS; MEDICAL DECISION MAKING; PLANNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COST-BENEFIT ANALYSIS; DECISION MAKING, COMPUTER-ASSISTED; DECISION THEORY; DECISION TREES; HUMANS; MODELS, BIOLOGICAL; SOFTWARE DESIGN; THROMBOPHLEBITIS; TIME FACTORS;

EID: 0029794705     PISSN: 0272989X     EISSN: None     Source Type: Journal    
DOI: 10.1177/0272989X9601600402     Document Type: Article

References (23)

1. Tversky A, Kahneman D. Judgement under uncertainty: heuristics and biases. Science. 1974;185:1124-31.
2. Kahneman D, Slovic P, Tversky A (eds). Judgement under Uncertainty: Heuristics and Biases. Cambridge, UK: Cambridge University Press, 1982.
3. Haddawy P, Suwandi M. Decision-theoreoc refinement planning using inheritance abstraction. Proc Second Int Conf on AI Planning Systems, Chicago, IL, June 1994, pp 266-71.
4. Hillner BE, Philbrick JT, Becker DM. Optimal management of suspected lower-extremity deep vein thrombosis: an evaluation with cost assessment of 24 management strategies. Arch Intern Med. 1992;152:165-75.
5. Landefeld S, McGuire E, Cohen AM Clinical findings associated with acute proximal deep vein thrombosis: a basis for qualifying clinical judgment. Am J Med. 1990;88:382-8.
6. Weinmann EE, Salzman EW. Deep-vein thrombosis. N Engl J Med. 1994;331:1630-41.
7. Pauker SG, Kassirer JP. Decision analysis. N Engl J Med. 1987;316:250-8.
8. Lau J, Kassirer JP, Pauker SG. Decision maker 3.0. Med Decis Making 1983;3:39-43.
9. Hanks S. Projecting Plans for Uncertain Worlds. PhD thesis, Yale University, New Haven, CT, January 1990.
10. Goldman RP, Boddy MS. Epsilon-safe planning. Proc Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, July 1994, pp 253-61.
11. Kushmerick N, Hanks S, Weld D. An algorithm for probabilistic least-commitment planning. Proc Twelfth Nat Conf on Artificial Intelligence. Seattle, WA, American Association for Artificial Intelligence, 1994, pp 1073-8.
12. Keeney RL, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Wiley, 1976.
13. Haddawy P, Doan A. Abstracting probabilistic actions. Proc Tenth Conf on Uncertainty in Artificial Intelligence, Seattle, WA, July 1994, pp 270-7.
14. Finigan MG. Knowledge acquisition for decision-theoretic planning. Proc MAICSS'95, Carbondale, IL, April 1995, pp 98-102.
15. Doan A, Haddawy P. Generating macro operators for decision-theoretic planning. Working Notes of the AAAI Spring Symposium on Extending Theories of Action, Stanford, CA, American Association for Artificial Intelligence, March 1995, pp 68-73.
16. Kahn CE Jr, Haddawy P. Management of suspected lower-extremity deep venous thrombosis (letter). Arch Intern Med. 1995;155:426.
17. Lawler EL, Wood DE. Branch-and-bound methods: a survey. Oper Res. 1966;14:699-719
18. Wellman MP. Formulation of Tradeoffs in Planning under Uncertainty. London, UK: Pitman, 1990.
19. Drummond M, Bresina J Anytime synthetic projection: maximizing the probability of goal satisfaction. Proc Eighth Nat Conf on Artificial Intelligence, Boston, MA, American Association for Artificial Intelligence, July 1990, pp 138-44.
20. Dean T, Kaelbling LP, Kirman J, Nicholson A. Planning with deadlines in stochastic domains. Proc Eleventh Nat Conf on Artificial Intelligence, Washington, DC, July 1993, pp 574-9.
21. Boutilier C, Dearden R. Using abstractions for decision-theoretic planning with time constraints. Proc Twelfth Nat Conf on Artificial Intelligence, Seattle, WA, American Association for Artificial Intelligence, Juiy 1994, pp 1016-22.
22. Cassandra AK, Kaelbling LP, Littman ML. Acting optimally in partially observable stochastic domains. Proc Twelfth Nat Conf on Artificial Intelligence, Seattle, WA, American Association for Artificial Intelligence, July 1994, pp 1023-8.
23. Uckun S. Instantiating and monitoring treatment protocols. Proc Eighteenth Annu Symp on Computer Applications in Medical Care (SCAMC94), Washington, DC, November 1994, pp 689-93.