A graphical decision-theoretic model for neonatal jaundice

Medical Decision Making

Volumn 27, Issue 3, 2007, Pages 250-265

  Gómez, Manuel ^a   Bielza, Concha ^a   Fernández Del Pozo, Juan A ^a   Ríos Insua, Sixto ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Clinical decision support systems; Decision analysis; Decision making; Influence diagrams; Multiattribute utilities; Neonatal jaundice; Socioeconomic factors

Indexed keywords

ARTICLE; DECISION SUPPORT SYSTEM; EXCHANGE BLOOD TRANSFUSION; HOSPITAL ADMISSION; HUMAN; INTERVIEW; LENGTH OF STAY; MEDICAL DECISION MAKING; MEDICAL PRACTICE; NEWBORN JAUNDICE; PHOTOTHERAPY; SOCIOECONOMICS;

DECISION SUPPORT SYSTEMS, CLINICAL; DECISION SUPPORT TECHNIQUES; DISEASE MANAGEMENT; EUROPE; HUMANS; INFANT, NEWBORN; INTERVIEWS; JAUNDICE, NEONATAL;

EID: 34249032769     PISSN: 0272989X     EISSN: 1552681X     Source Type: Journal    
DOI: 10.1177/0272989X07300605     Document Type: Article

References (45)

1. Gartner LM Neonatal jaundice. Pediatr Rev. 1995 ; 16: 22 - 31.
2. Mollison PL, Cutbush M. A method of measuring the severity of a series of cases of hemolytic disease of the newborn. Blood. 1951 ; 6: 777 - 88.
3. Rubaltelli FF, Zanardo V., Granati B. Effect of various photo-therapy regimens on bilirubin decrement. Pediatrics. 1978 ; 61: 838 - 41.
4. Newman TB, Maisels MJ Evaluation and treatment of jaundice in the term infant: a kinder, gentler approach. Pediatrics. 1992 ; 89: 809 - 30.
5. American Academy of Pediatrics, Subcommittee on Hyperbilirubinemia. Clinical practice guideline: management of hyperbilirubinemia in the newborn infant 35 or more weeks of gestation. Pediatrics. 2004 ; 114 (1). 297 - 316.
6. Worsley JB Hyperbilirubinemia guidelines and unintended harms. Pediatrics. 2004 ; 114 (4). 1134 - 5.
7. Alkalay AL, Simmons CF Hyperbilirubinemia guidelines in newborn infants. Pediatrics. 2005 ; 115 (3). 824 - 5.
8. Clayton P., Hripsak G. Decision support in healthcare. Int J Biomed Comput. 1995 ; 39: 59 - 66.
9. Ball MJ, Lillis J. E-health: transforming the physician/patient relationship. Int J Med Inform. 2001 ; 61: 1 - 10.
10. Ruland CM A survey about the usefulness of computerized systems to support illness management in clinical practice. Int J Med Inform. 2004 ; 73: 797 - 805.
11. Howard RA, Matheson JE Influence diagrams. In: Howard RA, Matheson JE, eds. The Principles and Applications of Decision Analysis. Vol. 2. Menlo Park (CA): Strategic Decisions Group; 1984.
12. Clemen RT Making Hard Decisions: An Introduction to Decision Analysis. 2 nd ed. Pacific Grove (CA): Duxbury ; 1996.
13. Sonnenberg FA, Hagerty CG, Kulikowski CA An architecture for knowledge-based construction of decision models. Med Decis Making. 1994 ; 14: 27 - 39.
14. Bielza C., Shenoy PP A comparison of graphical techniques for asymmetric decision problems. Manage Sci. 1999 ; 45 (11). 1552 - 69.
15. Nielsen TD, Jensen FV Representing and solving asymmetric decision problems. Int J Inform Technol Decis Making. 2003 ; 2 (2). 217 - 63.
16. Demirer R., Shenoy PP Sequential valuation networks for asymmetric decision problems. Eur J Oper Res. In press.
17. Keeney RL, Raiffa H. Decisions with Multiple Objectives. Preferences and Value Tradeoffs. 2 nd ed. Cambridge (UK): Cambridge University Press ; 1993.
18. van der Gaag LC, Renooij S., Witteman Clm, Aleman Bmp, Taal BG Probabilities for a probabilistic network: a case-study in oesophageal cancer. Artif Intell Med. 2002 ; 25 (2). 123 - 48.
19. Merkhofer MW Quantifying judgmental uncertainty: methodology, experiences, and insights. IEEE Trans Syst Man Cyber. 1987 ; 17: 741 - 52.
20. Shephard GG, Kirkwood CW Managing the judgmental probability elicitation process: a case study of analyst/manager interaction. IEEE Trans Eng Manage. 1994 ; 41: 414 - 25.
21. Morgan MG, Henrion M. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge (UK): Cambridge University Press ; 1990.
22. Jensen FV Bayesian Networks and Decision Graphs. Berlin: Springer; 2001.
23. Díez FJ Parameter adjustment in Bayes networks: the generalized noisy-OR gate. In: Heckerman D, Mamdani A, eds. Uncertainty in Artificial Intelligence. San Mateo (CA): Morgan Kaufmann ; 1993. p 99 - 105.
24. Pearl J. Probabilistic Reasoning in Intelligent Systems. San Mateo (CA): Morgan Kaufmann ; 1988.
25. Gómez M. IctNeo: A decision support system for newborn jaundice management [dissertation, in Spanish]. Madrid (Spain): Department of Artificial Intelligence, Technical University of Madrid; 2002.
26. Gómez M., Ríos-Insua S., Bielza C., Fernández del Pozo JA Multiattribute utility analysis in the IctNeo system. In: Haimes YV, Steuer RE, eds. Research and Practice in Multiple Criteria Decision Making. Lecture Notes in Economics and Mathematical Systems 487. Berlin: Springer ; 2000.
27. Delquié P., Luo M. A simple trade-off condition for additive multiattribute utility. Multicriteria Decis Analysis. 1997 ; 6: 248 - 52.
28. Farquhar PH Utility assessment methods. Manage Sci. 1984 ; 30: 1283 - 300.
29. Von Nitzsch R, Weber M. Utility function assessment on a micro-computer: an interactive procedure. Ann Oper Res. 1988 ; 16: 149 - 60.
30. Ríos S., Ríos-Insua S., Ríos Insua D, Pachón JG Experiments in robust decision making. In: Ríos S, ed. Decision Theory and Analysis: Trends and Challenges. Boston: Kluwer ; 1994. p 231 - 42.
31. Cooper GF The computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell. 1990 ; 42: 393 - 405.
32. Quaglini S., Bellazzi R., Locatelli F., Stefanelli M., Salvaneschi C. An influence diagram for assessing GVHD prophylaxis after bone marrow transplantation in children. Med Decis Making. 1994 ; 14: 223 - 35.
33. Fernández del Pozo JA, Bielza C., Gómez M. A list-based compact representation for large decision tables management. Eur J Oper Res. 2005 ; 160 (3). 638 - 62.
34. Bielza C., Gómez M., Ríos-Insua S., Fernández del Pozo JA Structural, elicitation and computational issues faced when solving large influence diagrams. Comput Oper Res. 2000 ; 27 (7-8). 725 - 40.
35. Felli JC, Hazen GB Sensitivity analysis and the expected value of perfect information. Med Decis Making. 1998 ; 18: 95 - 109.
36. Bielza C., Ríos-Insua S., Gómez M., Fernández del Pozo JA Sensitivity analysis in IctNeo. In: Ríos Insua D, Ruggeri F, eds. Robust Bayesian Analysis, Lecture Notes in Statistics 152. Berlin: Springer ; 2000.
37. Peña D. A decision analysis model for a serious medical problem. Manage Sci. 1980 ; 26 (7). 707 - 18.
38. Helfand M., Pauker SG Influence diagrams: a new dimension for decision models [editorial]. Med Decis Making. 1997 ; 17 (3). 351 - 2.
39. Molino G., Marzuoli M., Molino F., et al. Validation of Icterus, a knowledge-based expert system for jaundice diagnosis. Methods Inform Med. 2000 ; 39 (4 - 5). 311 - 8.
40. Marcos M., Balser M., ten Teije A., van Harmelen F. From informal knowledge to formal logic: a realistic case study in medical protocols. In: Gämez-Pírez A, Benjamins RV, eds. Lecture Notes in Artificial Intelligence 2473. Berlin: Springer ; 2002.
41. Cohen AM, Stavri PZ, Hersh WR A categorization and analysis of the criticisms of evidence-based medicine. Int J Med Inform. 2004 ; 73: 35 - 43.
42. Owens DK, Shachter RD, Nease RF Representation and analysis of medical decision problems with influence diagrams. Med Decis Making. 1997 ; 17: 241 - 62.
43. Nease RF, Owens DK Use of influence diagrams to structure medical decisions. Med Decis Making. 1997 ; 17: 263 - 75.
44. Payne JW, Bettman JR, Johnson EJ The Adaptive Decision Maker. Cambridge (UK): Cambridge University Press ; 1993.
45. Chenoweth T., Dowling KL, St. Louis RD Convincing DSS users that complex models are worth the effort. Decis Support Syst. 2004 ; 37: 71 - 82.