Communication devices and connectivity are increasingly ubiquitous, contributing to a rapidly-expanding infrastructure. This development promises to tackle some of the most challenging issues facing society today – how healthcare is delivered to an aging and expanding population. Each year, millions of people worldwide suffer from chronic long-term diseases, such as diabetes, heart disease, and kidney malfunction, with limited access to appropriate treatment. Machine learning plays a key role in determining how effective healthcare will be delivered to future generations. Reliable continuous tracking of patient health can provide accurate early warning of health deterioration. “Big data” (e.g.: electronic health records and data from wearable devices for monitoring chronic diseases and well-being) are now being collected, which cover the entirety of patient care, throughout the life of a patient. It is therefore necessary to develop novel machine learning methods to exploit the contents of these large complex datasets by performing robust, automated inference at very large scale.