Forecasting the Consumer Healthcare Journey with Recurrent Neural Networks

By Hadi EbrahimnejadForecasting Consumer Healthcare Journey with Recurrent Neural Networks

Use of artificial neural networks for machine learning has enabled major advancements in intelligent systems, helping millions of people in their daily lives. During the past decade, progress has greatly accelerated thanks to the availability of massive amounts of data and use of specialized hardware to build deeper networks and perform faster optimization. Furthermore, better insight into the inner workings of deep neural networks has enabled researchers and practitioners to achieve improvements in training and generalization.

There are numerous environments where systems powered by neural networks shape our experiences and influence our behavior. They routinely manifest themselves in our experiences with e-commerce, web search, as well as communicational interfaces such as smart speakers, messaging and email applications.

An important area where use of machine learning is still in its infancy is population health. While deep learning has been used for medical diagnosis applications, building predictive models for behavior of healthcare consumers is a relatively unexplored subject. This is a potential use case that we are passionate about at Accolade.

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