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Machine Learning Models for Human Disease Discovery and Prediction

Machine Learning Models for Human Disease Discovery and Prediction

 

There is enormous potential for machine learning approaches and in particular, artificial intelligence techniques, to improve health care and address other societal challenges. Successful development of techniques requires enormous amounts of data. In Europe, a framework for such sensitive Big Data applications has recently been established by the General Data Protection Regulation (GDPR). In the US, there is a different cultural background for data protection regulations, which has made it easier in some instances, to develop and implement machine learning-based frameworks for Big Data applications. In this project we will initiate an interaction between the US and the European perspective, in order to inspire technical developments and the education of students and future researchers for mutual benefit. From a subject matter perspective, the objective of this collaboration is to explore areas within healthcare where big data and machine learning methods can help to increase the speed, scalability and efficiency of disease detection and wellness planning.

 


Organisers

 

Prof. Dr. Harald Binder

University of Freiburg
Institute of Medical Biometry and Statistics (IMBI), Medical Biometry and Statistics

Email:

 

Prof. Dr. Conrad Tucker

Penn State University
NSF Center for Health Organization Transformation (CHOT)
Engineering Design, Industrial Engineering, Computer Science and Engineering

Email: ctucker4@psu.edu