Learning Conditional Probabilities for Dynamic Influence Structures in Medical Decision Models
Based on the DynaMoL (a Dynamic decision Modeling Language) framework, we examine the critical issues in automated learning of numerical parameters from large medical databases; present a Bayesian method for learning conditional probabilities from data; analyze how to elicit prior probabilities from...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
1997
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/3060 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Based on the DynaMoL (a Dynamic decision Modeling Language) framework, we examine the critical issues in automated learning of numerical parameters from large medical databases; present a Bayesian method for learning conditional probabilities from data; analyze how to elicit prior probabilities from the domain expert; and examine several important issues on pre-processing raw data for application in dynamic decision modeling. |
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