Please use this identifier to cite or link to this item:
https://doi.org/10.1016/S1386-5056(98)00085-9
DC Field | Value | |
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dc.title | Dynamic decision analysis in medicine: A data-driven approach | |
dc.contributor.author | Cao, C. | |
dc.contributor.author | Leong, T.-Y. | |
dc.contributor.author | Leong, A.P.K. | |
dc.contributor.author | Seow, F.C. | |
dc.date.accessioned | 2014-10-27T06:02:08Z | |
dc.date.available | 2014-10-27T06:02:08Z | |
dc.date.issued | 1998-07 | |
dc.identifier.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 | |
dc.identifier.issn | 13865056 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/99250 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S1386-5056(98)00085-9 | |
dc.source | Scopus | |
dc.subject | Abstraction | |
dc.subject | Bayesian learning | |
dc.subject | Databases | |
dc.subject | Dynamic decision analysis | |
dc.subject | Modeling | |
dc.type | Article | |
dc.contributor.department | INFORMATION SYSTEMS & COMPUTER SCIENCE | |
dc.description.doi | 10.1016/S1386-5056(98)00085-9 | |
dc.description.sourcetitle | International Journal of Medical Informatics | |
dc.description.volume | 51 | |
dc.description.issue | 1 | |
dc.description.page | 13-28 | |
dc.description.coden | IJMIF | |
dc.identifier.isiut | 000075503500002 | |
Appears in Collections: | Staff Publications |
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