Abstract: Over the last few decades, growing adoption of Electronic Health Record (EHR) systems has made massive clinical data available electronically. However, over 80% of clinical data are unstructured (e.g., narrative clinical documents) and are not directly assessable for computerized clinical applications. Therefore, natural language processing (NLP) technologies, which can unlock information embedded in clinical narratives, have received great attentions in the medical domain. Many NLP methods and systems have been developed in the medical domain. However, it is still challenging for new users to decide which NLP methods or tools to pick for their specific applications. In fact, there is a lack of best practices for building successful NLP applications in the medical domain.
In this 3-hour tutorial, we would like to introduce methods, tools, and best practices on building NLP solutions for clinical and translational research. We will start with an introduction of basic NLP concepts and available tools, and then focus on important applications of NLP in the medical domain such as phenotyping. We plan to use lectures, demonstrations and hands-on exercises to cover the basic knowledge/tools and use case studies to illustrate important trade-offs in the design and implementation of clinical NLP applications. Each instructor has over 10 years of experience in clinical NLP research and application and they will share their recommendations in building successful NLP applications in clinical research.

Learning Objective 1: Know the basic concepts of clinical NLP and can describe common uses of clinical NLP

Learning Objective 2 (Optional): Understand the challenges to clinical NLP research and development, and can describe the typical technical approaches and their pros and cons

Learning Objective 3 (Optional): Be familiar with existing NLP frameworks such as the Unstructured Information Management Architecture (UIMA) framework, annotation tools, and be familiar with common trade-offs in the design and implementation of clinical NLP application for research and operations


Hua Xu (Presenter)
The University of Texas Health Science Center at Houston

Hongfang Liu (Presenter)
Mayo Clinic

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