Please use this identifier to cite or link to this item:
|Title:||Dynamic decision analysis in medicine: A data-driven approach|
|Authors:||Cao, C. |
Dynamic decision analysis
|Citation:||Cao, C., Leong, T.-Y., Leong, A.P.K., Seow, F.C. (1998-07). Dynamic decision analysis in medicine: A data-driven approach. International Journal of Medical Informatics 51 (1) : 13-28. ScholarBank@NUS Repository. https://doi.org/10.1016/S1386-5056(98)00085-9|
|Abstract:||Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. Two major challenges in dynamic decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general dynamic decision modeling framework called DynaMoL (Dynamic decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to dynamic decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.|
|Source Title:||International Journal of Medical Informatics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 20, 2018
WEB OF SCIENCETM
checked on Apr 24, 2018
checked on May 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.