Learning temporal probabilistic causal models from longitudinal data

Artificial Intelligence in Medicine

Volumn 8, Issue 3, 1996, Pages 217-234

  Riva, Alberto ^a   Bellazzi, Riccardo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

Author keywords

Learning; Patient monitoring; Temporal probabilistic causal models; Time series analysis

Indexed keywords

INSULIN; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PATIENT MONITORING; PHYSIOLOGY; PROBABILITY; TIME SERIES ANALYSIS;

CAUSAL PROBABILISTIC NETWORKS; CONDITIONAL PROBABILITY LEARNING; TEMPORAL PROBABILITY CAUSAL MODELS;

ARTIFICIAL INTELLIGENCE;

ARTICLE; COMPUTER MODEL; COMPUTER SYSTEM; DIABETES MELLITUS; MEDICAL DECISION MAKING; MEDICAL INFORMATION; PATIENT MONITORING; PRIORITY JOURNAL; PROBABILITY;

EID: 0030200712     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/0933-3657(95)00034-8     Document Type: Article

References (35)

1. [1] S. Andreassen, M. Woldbye, B. Falck and S.K. Andersen, MUNIN: a causal probabilistic network for interpretation of electromyographic findings, Proc. 10th Int. Joint Conf. on Artificial Intelligence (1987) 366-372.
2. [2] S. Andreassen, Model-based biosignal interpretation, Methods of Information in Medicine 33 (1994) 103-110.
3. [3] R. Bellazzi, Drug delivery optimization through bayesian networks: an application to erythropoietin therapy in uremic anemia, Computers and Biomedical Research 26 (1993) 274-293.
4. [4] R. Bellazzi and G. De Nicolao, Smoothing noisy signals with Bayesian Networks, in: A. Gammerman, ed., Bayesian Belief Networks and Probabilistic Reasoning, pp. 157-182 (Unicom, Uxbridge, 1994).
5. [5] R. Bellazzi, C. Siviero, M. Stefanelli and G. De Nicolao Adaptive controllers for intelligent monitoring, Artificial Intell. Med., 1996, in press.
6. [6] R. Bellazzi and A. Riva, Learning conditional probabilities with longitudinal data. Workshop on Building Probabilistic Networks, IJCAI, Montreal, 1995, pp. 7-153.
7. [7] D. Bertsekas, Dynamic Programming (Prentice Hall, Engelwood Cliffs, NJ. 1990).
8. [8] C. Berzuini, R. Bellazzi and S. Quaglini, Temporal Reasoning with Probabilities, Proc. of V Workshop on Uncertainty in Artificial Intelligence (1989) 14-21.
9. [9] C. Berzuini, R. Bellazzi, S. Quaglini and D.J. Spiegelhalter. Bayesian networks for patient monitoring, Artificial Intelligence in Med. 4 (1992) 243-260.
10. [10] C. Berzuini, C. Larizza and P. Grossi, Bayesian prediction from complex biomedical data, Proc. IPMU 1994 (Paris, France, 4-8 July, 1994).
11. [11] W.L. Buntine, Operations for learning with graphical models, J. Artificial Intelligence Res. 2 (1994) 159-225.
12. [12] G. Consonni and P. Giudici, Learning in probabilistic expert systems, in: R. Scozzafava, ed., S.I.S. Workshop on Probabilistic Expert Systems (1993) 57-78.
13. [13] R.G. Cowell, A.P. Dawid and D.J. Spiegelhalter, Sequential model criticism in probabilistic expert systems, IEEE Trans. Pattern. Anal. Mach. Intell. 15 (1993) 209-219.
14. [14] P. Dagum and A. Galper, Additive belief-network models, Knowledge System Laboratory Report KSL-93-01, Stanford, CA, July 1993.
15. [15] P. Dagum, A. Galper, E. Horvitz and A. Seiver, Uncertain reasoning and forecasting, Knowledge System Laboratory Report KSL-93-47, Stanford, CA, June 1993.
16. [16] A.P. Dempster, Construction and local computation aspects of network belief functions, in: R.M. Oliver and J.Q. Smith, eds., Influence Diagrams, Belief Nets and Decision Analysis, (Wiley, 1990).
17. [17] D. Heckerman, D. Geiger and D.M. Chickering, Learning Bayesian networks: the combination of knowledge and statistical data, Microsoft Research Advanced Technology Division Report, Microsoft Corporation, MSR-TR-94-09, 1994.
18. [18] U. Kjærulff, A computational scheme for reasoning in dynamic probabilistic networks, Proc. VIII Workshop on Uncertainty in Artificial Intelligence (1992) 14-21.
19. [19] T. Leonard, Bayesian simultaneous estimation for several multinomial experiments, Commun. Statist.-Theor. Meth. (part a), A6(7) (1977) 619-630.
20. [20] L. Lenert, L. Sheiner and T. Blaschke. Improving drug dosing in hospitalized patients: automated modeling of pharmacokinetics for individualization of drug dosage regimens Computer Methods and Programs in Biomedicine 30 (1989) 169-176.
21. [21] L. Ljung, System Identification - Theory for the user (Prentice Hall, Englewood Cliffs, NJ, 1987).
22. [22] U.G. Oppel, A. Hierle, L. Janke and W. Moser, Transformation of compartmental models into sequences of causal probabilistic networks, in: S. Andreassen. R. Engelbrecht and J. Wyatt, eds., Artificial Intelligence in Medicine (IOS Press, Amsterdam, 1993) 319-330.
23. [23] G.M. Provan, Tradeoffs in knowledge-based construction of probabilistic models, IEEE Trans. Sys. Man Cybern. 24 (1994) 1580-1592.
24. [24] S.L. Normand and D. Tritchler, Kalman filtering in a Bayesian network, Technical Report, Dept. Of Preventive Medicine and Biostatistics, University of Toronto, Canada, 1989.
25. [25] J Pearl, Probabilistic Reasoning in Intelligent Systems (Morgan Kaufmann, Palo Alto, CA, 1988).
26. [26] S. Quaglini, R. Bellazzi, C. Berzuini, M. Stefanelli and G. Barosi, Hybrid knowledge-based systems for therapy planning, Artificial Intelligence in Med. 4 (1992) 207-226.
27. [27] S. Quaglini, R. Bellazzi, F. Locatelli, M. Stefanelli and C. Salvaneschi, An influence diagram for assessing GVHD prophylaxis after bone marrow transplantation in children, Medical Decision Making 14 (1994) 223-235.
28. [28] M. Ramoni and A. Riva, Belief maintenance with probabilistic logic, Proc. AAAI Fall Symposium on Automated Deduction in Non-Standard Logics, pp. 142-148 (Raleigh, NC, 1993).
29. [29] A. Riva and R. Bellazzi, High level control strategies for diabetes therapy, AIME-95 Conference, pp. 185-196 (Pavia, June, 1995).
30. [30] L. Sheiner and S.L. Beal, Bayesian individualization of pharmacokinetics: simple implementation and comparison with non Bayesian methods, J. Pharm. Sci. 71 (1982) 1344-1348.
31. [31] D. Spiegelhalter, A. Dawid, S. Lauritzen and R. Cowell, Bayesian analysis in expert systems. Statistical Science 8 (1993) 219-283.
32. [32] D. Spiegelhalter and S. Lauritzen. Sequential updating of conditional probabilities on directed graphical models, Networks 20 (1990) 579-605.
33. [33] J.Q. Smith, statistical principles on graphs (with discussion), in: R.M. Oliver and J.Q. Smith eds., Influence Diagrams, Belief Nets and Decision Analysis (Wiley, 1990).
34. [34] J.A. Tatman and R.D. Shachter, Dynamic programming and influence diagrams, IEEE Trans. Sys. Man Cybern. 20 (1990) 365-379.
35. [35] D. Worthington, The use of models in the self-management of insulin-dependent diabetes mellitus, Comp. Meth. Prog. Biomed. 32 (1990) 233-239.