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|Title:||Constructing influence views from data to support dynamic decision making in medicine||Authors:||Qi, X.
Branch and Bound
Dynamic Decision Making
Minimal Description Length Principle
|Issue Date:||2001||Citation:||Qi, X.,Leong, T.-Y. (2001). Constructing influence views from data to support dynamic decision making in medicine. Studies in Health Technology and Informatics 84 : 1389-1393. ScholarBank@NUS Repository. https://doi.org/10.3233/978-1-60750-928-8-1389||Abstract:||A dynamic decision model can facilitate the complicated decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a dynamic decision model from a large medical database. Within the DynaMoL (a dynamic decision modeling language) framework, a model can be represented in influence view. Thus, our proposed approach first learns the structures of the influence view based on the minimal description length (MDL) principle, and then obtains the conditional probabilities of the model by Bayesian method. The experiment results demonstrate that our system can efficiently construct the influence views from data with high fidelity. © 2001 IMIA. All right reserved.||Source Title:||Studies in Health Technology and Informatics||URI:||http://scholarbank.nus.edu.sg/handle/10635/78069||ISBN:||1586031945||ISSN:||09269630||DOI:||10.3233/978-1-60750-928-8-1389|
|Appears in Collections:||Staff Publications|
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