Machine Learning for Biomedical Applications

Dear Colleagues,

This Special Issue of Electronics on Machine Learning for Biomedical Applications aims to showcase and synthesize the pillars of the end-to-end connected pipeline of sensor data collection and/or storage, processing, decision-making, actuation, and security of biomedical sensor systems. In this issue, we will explore current trends and challenges in biomedical systems and machine learning tools and techniques for processing their data in biomedically meaningful ways, with a specific aim of facilitating practical application, expansion, and scale of the techniques presented. The purpose of this Special Issue is to enable researchers to a) rapidly define the current state-of-the-art in wireless/ubiquitous sensor systems for biomedical applications, b) to apply machine learning to wireless/ubiquitous sensor signals for real-time or near-real-time decisioning, and c) to identify current trends and gaps for future work in learning techniques, security and privacy, cloud- and edge processing, and considerations for efficient computing in constrained environments. An omnipresent concern underlying each of these facets is the need for responsible machine learning and transparent decisioning as well as effective human–machine interfaces to promote a safe and seamless user experience, and this issue aims to highlight the current state and ongoing need for responsible and ethical development and use of machine learning. Accepted articles will build upon existing literature by offering surveys of existing work across each pillar of the ecosystem and showcasing compelling biomedical applications that build upon leading-edge tools and techniques as well as the unique challenges of applying machine learning to biomedical applications such as generalization of training data, using unsupervised learning for real-time and near-real-time applications, and adapting estimates to noisy ground truth observations. In addition, wireless systems present unique challenges including security and privacy considerations on collected data, power constraints on ubiquitous, wearable, or passive wireless sensors, and shifting computation between the cloud, edge, and hybrid environments as well as between the physical and processing layers. By exploring novel solutions to these unique challenges, we seek to enhance the current state of machine learning systems in biomedical applications as well as basic research in machine learning techniques broadly. Thus, this Special Issue presents a holistic view of human-centric machine learning on biomedical systems.

Dr. William M. Mongan 
Guest Editor

Manuscript Submission Information

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• biomedical sensor systems
• biomedical machine learning
• wireless Internet-of-Things sensors and actuators
• wearable sensors
• wireless biomedical security and privacy
• passive and energy-constrained biomedical sensor systems
• human-centric biomedical wireless machine learning