An integration engineering framework for machine learning in healthcare

Abstract

Clinical models are technical systems that need to be integrated into the existing <i>system of systems</i> in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

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