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, Ljiljana MAJNARIC & Igor JURISICA

The session aims to bring together medical doctors with machine learning experts.

Health provides one of the most challenging, and rewarding, applications of machine learning (ML). ML-Systems applied to the biomedical field provide future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development – towards personalized and precision medicine. 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.

Successful ML bring AI directly into the workflow of the medical expert, hence sucessful applications have to consider the whole MAKE-Pipeline, i.e. from data fusion (e.g. genetic data fused together with patient record data) to visualization.

In addition to application papers, manuscripts dealing with fundamental questions and theoretical aspects in machine learning, as needed for successful health applications are encouraged.

Research topics – with application to health informatics – covered by this special session include but are not limited to the following topics:

  • Multi-Task Learning
  • Multi-View Learning
  • Transfer Learning
  • Multi-Agent-Hybrid Systems
  • Disease Models, e.g. Tumor Growth
  • Multi-Scale Data Integration & Data Fusion
  • 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