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A data preprocessing framework for supporting probability-learning in dynamic decision modeling in medicine
2000
Proceedings. AMIA Symposium
Data preprocessing is needed when real-life clinical databases are used as the data sources to learn the probabilities for dynamic decision models. Data preprocessing is challenging as it involves extensive manual effort and time in developing the data operation scripts. This paper presents a framework to facilitate automated and interactive generation of the problem-specific data preprocessing scripts. The framework has three major components: 1) A model parser that parses the decision model
pmid:11080021
pmcid:PMC2243981
fatcat:nfphbawxxferhi372raxusnyju