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- W69887310 abstract "This paper investigates the impact of different variables in Medical Device Development (MDD), where FDA (Food and Drug Administration) approval time is considered as a performance variable. To analyze the significance of the variables supervised Bayesian learning, the Minimal Description Length (MDL) algorithm, is used. A set of real FDA data, representing 474 different companies in USA medical device markets, from 2400 FDA approved orthopedic devices is used. The aim of the study is to identify which product, company and regulation factors contribute most to the variations in FDA decision time." @default.
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- W69887310 date "2013-08-19" @default.
- W69887310 modified "2023-10-11" @default.
- W69887310 title "Critical Factor Identification in Medical Device Development Through Supervised Learning" @default.
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