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Does artificial intelligence help clinicians to recognize atrophic gastritis with thyroid disease?
Date:2/27/2008

representation of variables and optimal sample size when dealing with complex and non-linear problems.

The results emerging from the study suggest artificial neural networks are able to predict, with a good accuracy, the presence of TD in ABG patients, by using clinical and gastric biochemical and histological variables. Indeed, the optimized ANN yielded an accuracy of 76%, correctly identifying 82% of ABG patients with TD, outperforming the previous ANN models as well as the traditional linear models.

This study did not want to emphasize that advanced statistical decision support systems should replace or substitute experienced clinicians, but to underline that these systems should be viewed as a potential decision aid to better address investigations to save costs and to use resources when effectively necessary.


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Contact: Jing Zhu
wjg@wjgnet.com
86-105-908-0039
World Journal of Gastroenterology
Source:Eurekalert

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