Explainable/Interpretable Machine Learning for Biomedical Sensing, Sensor Data Fusion and Diagnostics: 2nd Edition

Dear Colleagues,

In 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 the 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 possibilities, 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., via 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 in relation to explainable/interpretable machine learning. This includes studies with a focus on the following:

• 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 also welcome.

If you are planning a contribution but are not sure if it is relevant to the Special Issue, feel free to contact the Guest Editors.

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.