1:30 p.m. - 5:00 p.m. ET
H. Xu, The University of Texas Health Science Center at Houston; H. Liu, Mayo Clinic
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 workshop, 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.
11:30 a.m. - 3:00 p.m. ET
C. Parra-Calderón, Institute of Biomedicine of Seville / Virgen del Rocío University Hospital; C. Chronaki, HL7 Foundation; U. Tachinardi, Regenstrief Institute; N. Bahroos, University of Southern California; A. Solomonides, NorthShore University HealthSystem
We are living in a historic time worldwide caused by the coronavirus pandemic. The challenge is enormous worldwide and is testing the health systems and the economy of the affected countries themselves. It is also affecting the regions globally.
It is seriously challenging the capacity of the world's nations to collaborate in science to know and combat this enemy of Humanity. Therefore, the international dimension of knowledge and information exchange as well as the acceleration of evidence generation should be a central concern.
At this time, the FAIR principles are positioned as the pillar on which to base this necessary alignment of policies for managing data and health research results, now more essential than ever. This workshop will present an approach to the common components that should govern these FAIR policies between the US and Europe as well as the key aspects of their effective application.