Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT

Keyphrases

• Patient Data 100%
• Prediction Model 100%
• Personalized Medicine 100%
• Machine Learning Applications 100%
• Distributed Learning 100%
• Multicentric 100%
• Rapid Learning Healthcare 100%
• Learning Methodology 100%
• Support Vector Machine Model 100%
• Privacy-preserving 100%
• Area under the Curve 50%
• Netherlands 50%
• Germany 50%
• Cancer Patients 50%
• Belgium 50%
• Patient Demographics 50%
• Learning Algorithm 50%
• Distributed Databases 50%
• Data Sharing 50%
• Dyspnea 50%
• Learning Study 50%
• Lung Cancer Patients 50%
• Machine Learning 50%
• Radiation Oncology 50%
• Post-radiotherapy 50%
• IT Infrastructure 50%
• Population Needs 50%
• Discriminative Performance 50%
• Centralized Learning 50%
• Ethical Boundaries 50%
• Local Database 50%
• Data Privacy 50%
• Big Data Infrastructure 50%
• Distributed Datasets 50%
• Personalized Radiation Oncology 50%
• Distributed Learning Algorithm 50%
• Alternating Direction multiplier Method 50%
• Legal Boundaries 50%

Computer Science

• Health Care 100%
• Privacy Preserving 100%
• Distributed Learning 100%
• Machine Learning 100%
• Learning System 100%
• Prediction Model 66%
• Learning Algorithm 66%
• Support Vector Machine 66%
• Cancer Patient 66%
• Data Infrastructure 33%
• Large Data Set 33%
• Local Database 33%
• Data Sharing 33%
• Fold Cross Validation 33%
• Big Data 33%
• Distributed Databases 33%
• Alternating Direction Method of Multipliers 33%
• Data Privacy 33%

INIS

• data 100%
• learning 100%
• radiations 62%
• datasets 50%
• patients 37%
• machine learning 37%
• algorithms 25%
• cancer 25%
• applications 25%
• variations 25%
• vectors 25%
• medicine 25%
• prediction 25%
• lungs 12%
• curves 12%
• validation 12%
• comparative evaluations 12%
• netherlands 12%
• populations 12%
• germany 12%
• radiotherapy 12%
• performance 12%
• legal aspects 12%
• belgium 12%
• expansion 12%