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publications

Validation Methods to Promote Real-world Applicability of Machine Learning in Medicine

Published in DMIP '20: 2020 3rd International Conference on Digital Medicine and Image Processing, 2021

The impact of Artificial Intelligence (AI) on health care has been dramatic; however, there is a considerable degree of skepticism among clinicians about the real-world applicability of advanced predictive models; for this reason, it is particularly important to emphasize the need for proper model validation in machine learning. In this study, we provide an overview of the common limitations of model validation methods in medicine. We then present solutions to address such limitations, with a focus on strengthening the validity of predictive models.

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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