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Question: You are working with an intensive care unit (ICU) attending physician on a project to see if you can predict readmission for patients with pancreatitis. You have access to a large database of ICU data (such as cardiac catheter values, vital signs, and respiratory parameters), as well as all of the data that can be gleaned from the LIS. There are approximately 800 measurements of various types for each of 4000 patients. You do not really have any specific ideas about what values would be most predictive; in fact, you think it is likely that the predictors are highly complex combinations of factors. Which of the 3 types of artificial intelligence systems would be most appropriate for this problem, and why?

Answer: A neural network is most appropriate, because there is no prior knowledge to allow selection of predictors, the relative weighting of predictors is unknown, a large data set of many discrete potential predictors is available, combinations of predictors may provide better discrimination than individual predictors, and the desired classification is binary (readmission likely or unlikely).

A neural network is most appropriate, because there is no prior knowledge to allow selection of predictors, the relative weighting of predictors is unknown, a large data set of many discrete potential predictors is available, combinations of predictors may provide better discrimination than individual predictors, and the desired classification is binary (readmission likely or unlikely). Source: CoolInterview.com

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