MAKE-Health

Machine Learning & Knowledge Extraction for Health*

*) Health includes all aspects of complete physical, mental and social well-being (WHO definition)
Workshop organized by Andreas HOLZINGER & Igor JURISICA

This session is dedicated to bring together medical doctors with machine learning experts.

Health is amongst the greatest application challenges of machine learning, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. Health systems worldwide are challenged by big data and increasing amounts of unstructured information. Biomedicine and the Life Sciences are increasingly turning into a “big data” science. However, sometimes the problem is not big data but complex data: heterogeneous, high-dimensional, probabilistic, incomplete, uncertain and noisy data. The effective and efficient use of ML-algorithms for solving complex problems in health informatics are a commandment of our time and may support evidence-based decision-making and help eventually to realize the grand goals of personalized medicine: in modelling the complexity of patients to tailor medical decisions, health practices and therapies to the individual patient.

Research topics covered by this special session include but are not limited to the following topics. Papers which deal with fundamental questions and theoretical aspects in machine learning are very welcome.

  • Multi-Task Learning
  • Multi-View Learning
  • Transfer Learning
  • Multi-Agent-Hybrid Systems
  • Disease Models, e.g. Tumor Growth
  • Multi-Scale Data Integration
  • Uncertainty Propagation
    Decision Support
  • Empirical Inference and Causal Discovery
  • Socioeconomic factors

Accepted Papers will be published in the Springer CD-MAKE Volume of Lecture Notes in Artificial Intelligence (LNAI). We are planning to invite outstanding contributions for extension in journals (Springer MACH, BMC MIDM, tba.)

For submission details please proceed to the CD-MAKE authors area