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Large Scale Clinical Text Processing and Process Optimization

and Objective This tutorial outlines the benefits and challenges of processing large volumes of clinical text with natural language processing (NLP). As NLP becomes more available and is able to tackle more complex problems, the ability to scale to millions of clinical notes must be considered. The Department of Veterans Affairs (VA) has more than 2 billion clinical notes has developed NLP libraries to be able to apporach projects of that scale. Participants will be introduced to existing tools and resources for large-scale NLP tasks, including Unstructured Information Management Architecture Asynchronous Scaleout (UIMA AS), the VA Leo NLP libraries, and the JMX Analysis Module (JAM) monitoring tool. The methods of computational performance analysis will be described and process optimization solutions will be demonstrated. Participants will be walked through a scenario of creating and launching an asynchronous NLP pipeline, monitoring it for performance metrics and identifying bottlenecks, and redeploying the pipeline with an optimal configuration. The tutorial will be presented by two instructors involved in the development and use of Leo and JAM in the VA.