The Changing Landscape of Data Science in Medicine

Let's start 2020 strong by kicking off our first meetup event with Dr. Sharon Hensley Alford, Clinical Principal Data Scientist from IBM Watson Health. https://www.linkedin.com/in/hensleyalford/

The world's economy is still rapidly growing, so is science, especially data science. With the urgent situation of the Coronavirus, how can data science help with the control and prevention strategy? What data science techniques are commonly used in medicine and epidemiology? How do clinical scientists leverage data science to better evidence-based medicine through clinical trial designs? Do you like me have so many questions about medicine and epidemiology? Then this meetup event is for you!

Abstract
Medicine lags behind other industries in the adoption, standardization, and application of data. However, new laws in the US are changing the quality and quantity of data available for analysis. Sharon will share experiences and discuss the changing landscape of data science in medicine.

Hope to see you there. Feel free to bring a friend, but they must RSVP on Meetup by the day before. ALL GENDERS are welcome to join!

Agenda:

• 4:30 pm - 5:00 pm: Networking
• 5:00 pm - 6:00 pm: Speaker Presentation
• 6:00 pm - 6:30 pm: Q&A

Sharon's bio:
Sharon is a PhD epidemiologist with deep knowledge of medical data with 20 years of experience in health care and life science industries. At IBM Watson Health, Dr. Alford supports the data collection, analysis, and publication strategy for life science offerings. She is also involved in the application of IBM's advanced analytic capabilities to life science problems. Dr. Alford has been working on data and analytic strategy for life science clients since she joined IBM more than 2 years ago. Prior to IBM, she worked for 15 years at an integrated health system using clinical, claims, molecular, and patient reported data for academic and industry motivated research. Dr. Alford has more than 50 peer-reviewed publications and is interested in the application of machine learning in epidemiology. Her area of greatest clinical depth is oncology but she has worked in multiple disease domains.

Specialties:
Research study
Clinical practices & procedures
Pharmacoepidemiology
Health system and pharmacy data
Oncology
Real world evidence
Health outcomes research
Innovative use of data
Data analytics/ Analyzed data
Data interpretation

Please comment down below if you have any questions or thoughts!