Health and Public Health Applications for Decision Support Using Machine Learning

Dear Colleagues,

Well-tuned sensors optimized to translate bioinformation into the digital world are constantly evolving. This kind of technology provides improved data on humans, monitors the influence of the environment on humans and public health, and provides an excellent tool supporting the diagnosis of several diseases. Such devices can acquire data from a wide range of sources; with the help of high-level signals and image processing techniques, they can extract valuable data to feed artificial intelligence system models, with the aim of supporting decision making in various areas. This Special Issue is dedicated to covering the latest findings and emerging concepts relating to sensors and machine learning applications in health and public health areas. Topics may include, but are not limited to, the following:

• Signals: EEG, EMG, ECG, and evoked potential analysis;
• Medical imaging: X-ray, PET, CT, MRI, and SPECT;
• Epidemiological and environment data: temperature, humidity, NOx, pH, VOC signals, and epidemiological time series and their relationship with health and public health;
• Wearable applications.

Prof. Dr. Pedro Miguel Rodrigues
Prof. Dr. João Alexandre Lobo Marques
Prof. Dr. João Paulo do Vale Madeiro
Guest Editors

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