Speaker "Daniel Shenfeld" Details Back



AI in healthcare services - innovation with focus on business value


We discuss a machine learning product to decide which request for a medical procedure are clinically appropriate based on clinical information from the patient’s medical record provided by a human user. We describe the process to build and deploy this product within a low risk tolerance,non-technological corporate environment, and how focus on data-driven analysis, data quality and processes, and user experience can create incremental value in quick iterations and are a prerequisite for deriving value from machine learning at scale. We will also compare various deep architectures to identify whether free text from a patient’s clinical record provides supporting evidence for the clinical necessity of a given procedure.


Daniel leads the data science department at eviCore, the largest medical benefits management company in the US. In this role, he oversees the end-to-end development of strategic technological products including core machine learning components. Previously, Daniel led data science and R&D teams in the health IT space, from early stage ventures to companies in their growth phase. He has also consulted to various companies in the healthcare space as well as other verticals on machine learning, R&D management, and data strategy. Daniel holds a PhD in mathematics from Princeton University and an MS in computational biochemistry from the Hebrew University. His contributions have been published in Cell, Nature Biotechnology, and Physical Reviews, among others.