UCSF

Two information technology revolutions are colliding in medicine. The first revolution has been the digitalization of health data, specifically Electronic Health Records (EHR). These records contain the details of who we are as patients, our ailments, treatments, and outcomes. Tragically, despite billions of dollars in investment from the US government, hardly any of this data is being utilized to better understand medicine or improve healthcare. This is largely because the data is voluminous, sparse, complex, and poorly formatted; making it unsuitable for traditional analytics methods. However the second revolution, modern Artificial Intelligence, specifically deep learning, provides tools, in the form of algorithms, to address exactly these problems. The primary difference between these modern algorithms and older ones is that the former are able to learn, more or less on their own, how to transform large complex data into a format that makes it easier to use and learn from.

In this dissertation, I have developed methods to apply deep learning to digital health data. Doing so, I have shown that we can predict the future health of individual patients with highly complex diseases, produced approaches to understand and leverage what these complex models are learning, and provided a framework for how healthcare systems of the near future could automatically learn to improve care daily.

For the first time in history, we are in a position to learn from the combined knowledge of tens of thousands of physicians and their experiences caring for hundreds of millions of patients. The potential transformations to healthcare are difficult to fully fathom, but certainly include safer, more powerful and efficient medicine, and a rapid speed up in new medical discoveries and treatments. Despite the promise, we must proceed carefully, balancing the great need to collectively use our data for better medicine with the individual right to privacy.