IEEE CTS Computer Society (Austin) Chapter - â€Natural Language Processing and Machine Learning Techniques for Clinical & Pharmaceutical Researchâ€
In a special series addressing advanced techniques which assist with clinical trials and patient treatment, a gifted speaker will present a special talk on:
“Natural Language Processing and Machine Learning Techniques for Clinical & Pharmaceutical Research”
Presenter: Dr. Jennifer Davis, Senior data scientist and executive with domain expertise in Big Data, Life Sciences and Healthcare.
FREE F&B
Abstract:
This talk will give a high-level overview of some of the techniques used in modern digital technologies, which assist with clinical trials and patient treatment. Forward-looking possibilities and research questions will also be presented in the context of clinical and pharmaceutical cancer research. Medical health record data is truly ‘big’ data in that it is large in size, diverse in data type and source as well as high velocity (millions of patient records are shuttled across hospitals and institutions daily). While some data might be easily culled from medical records in the form of numerical representations, others are hidden within text information. In order to assist in clinical and pharmaceutics development, mining the data that is hidden away in text portions of medical record architecture or in free text notes is essential.
Informatics and natural language approaches have been successfully applied to diverse situations found in cancer research and informatics. A few include studies of patient morbidity and mortality as related to sentiment in patient records, creation of chemical compound libraries using optical character recognition software, creation of natural language processing software to process biomedical research literature and present topic-modeled assessments. These examples are just the very tip of what is possible for informatics research and data mining using natural language processing and machine learning.
Bio
Jennifer Davis, Ph.D., is a senior data scientist and executive with domain expertise in Big Data, Life Sciences and Healthcare. Jennifer graduated from Georgetown in Washington DC, having completed her dissertation at the Medical School in 2009 (Doctorate in Tumor Biology). Since then Jennifer has completed Post-doctoral Rotations and Research Fellowships at Baylor College of Medicine, MD Anderson Cancer Center, and The University of Texas, Austin both in the College of Pharmacy and the Cockrell School of Engineering. In 2013 Jennifer completed a Internship in Life Sciences Computing, at one of the largest super-computing centers in the world--the Texas Advanced Computing Center. Jennifer has a combined experience of over 10 years in computational methods including machine learning, statistics and data visualization. She has publications in international journals and is currently a reviewer for two international scientific journals, ‘Cancer Informatics’ and ‘PLoS One’. She is licensed in several areas of Data Science.
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- THE ADVISORY BOARD
- 12357-C Riata Trace Parkway
- Austin, Texas
- United States 78727
- Building: 7
- Room Number: 100
- Click here for Map
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Vinay Abburi (405) 334-8162
F.Behmann@ieee.org
- Co-sponsored by IEEE COMSOC/SP Austin
- Starts 05 April 2016 05:00 PM UTC
- Ends 27 April 2016 11:00 PM UTC
- No Admission Charge
Agenda
6:30 p.m. Networking and Gathering (with free food, drinks) 6:50 p.m. Call to Order, Announcement 7:00 p.m. Presentation, with Q/A 8:30 p.m. Meeting Evaluation, Adjourn