The workshop "Biomedical Knowledge Discovery from Big Data (BIOKDD)" deals with the dichotomy of potentials and threats of machine learning for biomedical data. There is an ongoing trend to produce and exploit medical data from a variety of distributed sources to achieve high quality personalized healthcare. These data are sensitive posing privacy security and trust threats. We want to bring together researchers from machine learning, medical imaging, bio-informatics and information security to discuss recent developments, best practices and use cases from on-going research projects.

 

TOPICS
Topics of BIOKDD’20 workshop includes, but not limited to:
• Trustworthy and explainable AI for medical data
• Privacy preserving machine learning
• Federated learning 
• Security and protection of shared personal medical data
• Integration of personal data from multiple sources into structured patient records
• New data analytics techniques for real-time personal healthcare data
• New authentication and trust mechanisms for medical data
• Compliance with emerging legal and ethical requirements for data protection
 
SUBMISSION GUIDELINES

Authors are invited to electronically submit original research contributions or experience reports in English. Submitted papers should not exceed 5 pages in Springer CCIS format. 
Formatting guidelines: http://www.dexa.org/formatting_guidelines
Online Papers Submission: https://easychair.org/conferences/?conf=biokdd2020

 

All accepted papers will be published by Springer in "Communications in Computer and Information Science (CCIS).  Authors of all accepted papers must sign a Springer copyright release form. At least one of the authors of an accepted paper must register to DEXA’20 conference and present the paper at BIOKDD’20 workshop.

For further enquiries please contact PC Chairs (biokdd2020@easychair.org)