Changing patient trajectory: A case study exploring implementation and deployment of clinical machine learning models

Yindalon Aphinyanaphongs

Abstract: You’ve created an awesome model that predicts with near 100 percent accuracy. Now what? In this tutorial, we will give insight into the implementation, deployment, integration, and evaluation steps following the building of a clinical model. Specifically, we will discuss each step in the context of informing design choices as you build a model. For example, aggressive feature selection is a necessary step toward integration as real time data streams of all the data points a machine learning model may consume may not be accessible or feasible. We will use our implementation and evaluation of a Covid-19 adverse event model at our institution as a representative case study. This case study will demonstrate the full lifecycle of a clinical model and how we transition from a model to affecting patient outcome and the socio-technical challenges for success.

Bio: Yindalon Aphinyanaphongs, MD, PhD (Predictive Analytics Team Lead) is a physician scientist in the Center for Healthcare Innovation and Delivery Science in the Department of Population Health at NYU Langone Health in New York City. Academically, he is an assistant professor and his lab focuses on novel applications of machine learning to clinical problems and the science behind successful translation of predictive models into clinical practice to drive value. Operationally, he is the Director of Operational Data Science and Machine Learning at NYU Langone Health. In this role, he leads a Predictive Analytics Unit composed of data scientists and engineers that build, evaluate, benchmark, and deploy predictive algorithms into the clinical enterprise.