Topical Advisory Panel Members’ Collection Series: Intelligent Sensing System for Healthcare

Dear Colleagues,

In recent years, life expectancy has steadily increased, and the demand for healthcare services is ever-growing. Smart Sensing technologies for healthcare monitoring include not only hardware (sensors, actuators, multimedia devices, etc.) but also applications for data acquisition, processing, transmission and presentation and applications for real-time decision support, disease prevention, symptom detection and diagnosis.

Among the technologies which play a crucial role in these applications are machine learning (ML) algorithms. Some applications would not have been possible without the help of ML models.

While modern ML methods, such as deep learning, are incredibly powerful, they are also seen as "black-box" models. This lack of transparency has made users wary of using them, particularly in healthcare applications and during the certification of devices.

To address this issue, the fields of explainable and interpretable machine learning (EML and IML) have emerged. Some classical ML models, such as decision trees and logistic regression, are inherently interpretable when used for problems with a limited number of features. For other models, interpretation can be facilitated through methods such as visualization or rule-based expressions. By increasing trust and transparency, EML and IML can provide new insights into unknown pathophysiological mechanisms. This knowledge can then be used to improve sensing and diagnostics, making EML and IML highly valuable.

For this Special Issue, we are seeking original contributions in the fields of smart sensing, biomedical sensing, diagnostics and sensor data fusion, preferably (but not exclusively) related to explainable or interpretable machine learning.

Topics of interest include (but are not limited to):

• Machine learning for healthcare;
• Artificial intelligence for healthcare;
• Security and privacy in health applications and technologies;
• Behavior analysis of patients;
• Health monitoring of patients;
• Non-invasive, minimally invasive sensing;
• Wearable sensors;
• Mobile healthcare devices;
• Explanation/interpretation of ML models;
• Improvements that have been or can be achieved through EML/IML;
• Methods to enhance explainability/interpretability;
• Theoretical analyses of EML/IML.

Dr. Giovanna Sannino
Prof. Dr. Yusuf Ozturk
Guest Editors

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