Artificial Intelligence for Mobile Health

Dear Colleagues,

Big data holds the key to unlocking the next level of diagnosis support systems. Big data-based diagnosis support systems are needed to address public health problems, such as cardiovascular disease, fever, obesity, and diabetes. That data comes from sensors that measure physiological signals, such as an electrocardiogram (ECG), heart rate (HR), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), from the human body. In order to be diagnostically relevant, the data must be communicated to a central location where it can be processed and accessed by a human expert. Effectively, this is the Internet of Medical Things that allows human experts and artificial intelligence algorithms to work cooperatively on diagnosis and treatment monitoring. Establishing that a symbiotic work relationship has the potential to improve outcomes for patients and reduce the number of years lived with disability.

Today, various machine learning and deep learning techniques have been applied for big data efficiently. Application of such novel methods to the medical data can aid the clinicians to make an accurate and fast diagnosis. Thus, this Special Issue, entitled “Artificial Intelligence for Mobile Health”, focuses on the application of advanced artificial intelligence algorithms, such as machine learning and deep learning techniques, in a mobile health setting.

Prof. U Rajendra Acharya
Dr. Oliver Faust
Guest Editors

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