Machine learning is a type of artificial intelligence in which computer algorithms are built to learn to perform set tasks, then use the data acquired to improve the performance of the processing of that data.

At the macro-level, ML can be applied to look at health records of different segments of the population (eg. elderly, pregnant, asthmatic, stroke etc.) to assist with evidence-based policy decisions. At a lower scale, ML techniques can be applied to data acquired from an individual patient's brain, heart, muscles, medical images etc. and cross referenced against "healthy" data to aid diagnosis.

Large amounts of information are often necessary for diagnosis and treatment of a condition. The volume and complexity of the data makes the physician's job difficult and provides a compelling reason for the development of machine learning based tools to aid them.

� propos des auteurs

Miguel Hernandez Silveira is the CEO and a principal consultant at Medical Frontier Technology Ltd, UK. He is also CTO of SENTI TECH LTD, UK. He held positions as visiting lecturer at the University of Surrey, UK, and a visiting researcher at Imperial College London, UK. He is also a member of the IET Healthcare Technical Profession Network Committee, and reviewer of IEEE Sensors and IEEE Biomedical Circuits and Systems Journals. His research interests include machine learning, wireless low-power healthcare systems, biomedical sensors, instruments and algorithms, and digital signal processing.

Su-Shin Ang is the CEO and a principal consultant at Medical Frontier Technology Asia Pte Ltd, Singapore. He is a practising engineer, whose passion lies in the application of cutting-edge technology to the improvement of patient care. His research interests include machine learning, healthcare technology, development and deployment of medical devices, and the Internet of medical things.

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