Explainable/Interpretable Machine Learning for Biomedical Sensing, Sensor Data Fusion and Diagnostics

Dear Colleagues,

Over recent years, many biomedical applications related to sensing, sensor data fusion and diagnostics have successfully invoked machine learning (ML) models. Some of them only became possible due to powerful ML models. In the future, the relevance of ML for such biomedical applications will further increase.

Modern ML methods like deep learning approaches are often black-box models. Despite their high performance, which exceeds current processing techniques and even human levels in some domains, users are often uncomfortable using them. This lack of trust is problematic in healthcare applications and during certification of devices.

As a solution to this problem, explainable or interpretable machine learning (IML) models and methods for interpretation, respectively, have been proposed. Some classical machine learning models like decision trees or logistic regression models inherently allow for interpretation, at least when used for problems with a small number of features. Regarding other models, which do not inherently feature interpretability, specific methods can foster interpretation, e.g., by visualization or rule-based expressions. Despite increasing trust, IML can help to reveal (unknown) pathophysiological mechanisms. The knowledge gained can flow back to enhanced sensing and diagnostics, which also makes IML of high interest.

For this Special Issue, we seek original contributions in the fields of biomedical sensing, diagnostics and sensor data fusion with a relation to explainable/interpretable machine learning. This includes works with a focus on

- The interpretation of applied/existing ML models

- Improvements that can be or even have been obtained by IML

- Methods to foster interpretability

- Theoretical analyses with respect to IML

Review articles with a focus on explainability/interpretability are welcome as well.

If you are planning a contribution but are not sure if it is in the focus of the Special Issue, feel free to contact the guest editors.

Prof. Dr. Christoph M. Friedrich
Prof. Dr. Sebastian Zaunseder
Guest Editors

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