Machine Learning in Healthcare: From Modeling to Clinical Impact

Narges Razavian / New York University Langone Medical Center

Abstract: Improved healthcare delivery and patient outcomes are the ultimate goals of many AI applications in healthcare. However, relatively few machine learning models have been translated to clinical practice so far and among those even fewer have undergone a randomized control trial (RCT) to assess their impact. This talk will highlight aspects of the clinical translational process, beyond retrospective modeling, that impact design, development, validation, and regulation of machine learning models in healthcare. In particular, this talk focuses on our recent study of predicting favorable outcomes in hospitalized COVID-19 patients. The resulting model, which was deployed and prospectively validated at NYU Langone, underwent an RCT, and was eventually shared with other institutions. I will discuss challenges around integrating our model in the EHR system and their implications, the efficacy and safety results of our RCT, and practical insights about sharing models across clinics. We will end the talk by reviewing results of a survey of over 195 clinical users who interacted with this model, summarizing when and how the model was most helpful.

Bio: Narges Razavian is an assistant professor at NYU Langone Health, Center for Healthcare Innovation and Delivery Sciences, and Predictive Analytics Unit. Her lab focuses on various applications of Machine Learning and AI for medicine with a clinical translation outlook, and they work with Medical Images, Clinical Notes, and Electronic Health Records. Before NYU Langone, she was a postdoc at CILVR lab at NYU Courant CS department. She received her PhD at CMU Computational Biology group.


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