Explainable/Interpretable Machine Learning for Biomedical Sensing, Sensor Data Fusion and Diagnostics: 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 99
Special Issue Editors
2. Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, 45122 Essen, Germany
Interests: machine learning; computational intelligence; biomedical applications; interpretable machine learning; natural language processing (NLP); computer vision; augmented reality; information extraction; information retrieval; image processing; biostatistics; bioinformatics; mathematics for computer science
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical applications; biomedical instrumentation; measurement technology; sensing; signal processing; image processing; machine learning; biophotonics; modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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Keywords
- interpretable machine learning (IML)
- explainable machine learning
- LIME
- shapley values
- feature importance
- knowledge integration
- visual interpretation support
- transparent ML models
- global/local explanations
- IML in regular contexts
- deep learning
- classical machine learning methods
- model agnostic models
- rule-based models
- feature interactions
- ensemble methods
- trusted models
- robustness of models
- biomedical applications
- diagnostics
- risk assessment
- healthcare
- wearable sensors
- Internet of Things (IoT)
- multisensor fusion
- data fusion
- anomaly detection
- audio processing
- computer vision
- image processing
- signal processing
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