Advanced Analytics

Advanced Analytics


The ATIA engineers have developed and are still expanding a set of tools that can be used for inductive and deductive reasoning:

  • These tools are especially useful to solve complex problems in the biomedical field, as the past three years of applying them have demonstrated.

  • They can discover interactions, meaning that one variable, for example anxiety, is only relevant if another variable, for example a certain type of brain activity, does not indicate a negative treatment outcome to start with.

  • The tools can also discover non-linear effects, for example that a certain rehabilitative treatment can not only start too late, but also too early after surgery.

  • The output of one tool/agent, may be used as input for another tool/agent, thus creating a multi agent system.



Since no two datasets are the same and no two problems are identical, ATIA has built a platform that allows us to do Advanced Analytics that we have named ADVANA. The  software of this platform finds characteristics in complex datasets and provides advisory information about new ( individual) cases to clinicians and patiensts. Using ADVANA, we can quickly build a reasoning configuration that prepares your data, mines your data for patterns and rules and provides the means to implement these as Decision support in your systems. 



  • New treatment approaches were found when ATIA analysed treatment outcome of combined repetitive Transcranial Magnetic Stimulation with psychotherapy in depressive patients for Brainclinics.
    As expected for such a combined treatment, the predictors of success were both biological and psychological. However, the major gain here was the prediction of non-response, which appeared to depend on psychological (co-morbid anxiety) and biological (brainwaves indicative of impaired vigilance regulation) predictors. The latter could be interpreted to suggest psychostimulant treatment, this matches a recent finding that  EEG-guided treatment of depression with psychostimulants is more effective than treatment by protocol.

  • Decision trees could give valuable information about responsers and non-responders.

  • ATIA has made predictive models that show that valuable tests, are ordered for diagnostic purposes, do not contribute information to take the required clinical decision: a chance for cost reduction in health care.

  • The ATIA tools also support our own applied biomedical research: Dr. Koen Böcker is using homemade software that analyzes thousands of voxels in a brainscan image for non-linear associations with personal characteristics related to intelligence, emotional regulation, eating and sexual behavior. Here is an example image of a brain scan showing a positive relation to social rise relative to parents.

© 2015 Alan Turing Institute Almere