Hi there, I’m Eike.

I am a postdoctoral researcher at DTU Compute, in the section for Visual Computing. In my current research within the project Bias and Fairness in Medicine, I (together with my amazing collaborators) attempt to answer the following questions:

What does it mean for a data-driven health risk score model to be “fair”? How can we test whether a model is fair, and how can we design such a model?

Allow me to elaborate just a tiny bit. Health risk score models are likely to be used for resource prioritization in the healthcare system, for example by influencing who gets access to extra preventive care and who does not.1 As these are decisions that influence human livelihoods, we – as a society – would obviously want these decisions to be made “fairly”. But what does fairness even mean in this context?

  • Should men and women (on average) get an identical amount of extra care?
  • What if a disease has a higher prevalence in a certain group?
  • What if we are better at identifying the high-risk patients in one group compared to another? Also, why would this happen in the first place?
  • What if the data that are available are biased due to (historical or present-day) societal biases, such as poor patients having worse access to medical treatment compared to rich patients?

Once the philosophical question of which fairness definition to pursue is answered, various technical questions arise: how can the fairness of a model be quantified, and how can we actively build a model that is fair in the chosen sense?

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Previously, I worked at the University of Lübeck, in the Institute for Electrical Engineering in Medicine. In a project executed together with the research unit of Dräger Medical, we worked hard to bring surface electromyographic monitoring of respiratory effort into clinical practice for improving mechanical ventilation. My research in this context spanned mathematical modeling, signal processing, parameter identification & statistical inference, all related to either surface electromyographic measurements, respiration, or both. See my previous publications for some of the work we did.

  1. I am not saying that I believe this is a good idea. I’m just saying: it’s likely to happen, and, in fact, already happening. That does not mean that we, as a society, should not actively decide whether we want it to happen or not