Machine learning advances are opening new routes to more precise healthcare, from the discovery of disease subtypes for patient stratification to the development of personalised interactions and interventions. As medicine pivots from treating diagnoses to treating mechanisms, there is an increasing need for personalised health through more intelligent feature extraction and phenotyping. This offers an exciting opportunity for machine learning techniques to impact healthcare in a meaningful way, by putting patients at the centre of research. Health presents some of the most challenging and under-investigated domains of machine learning research. This tutorial presents a timely opportunity to engage the machine learning community with the unique challenges presented within the healthcare domain as well as to provide motivation for meaningful collaborations within this domain. We will evaluate the current drivers of machine learning in healthcare and present machine learning strategies for personalised health. Some of the challenges we will address include, but are not limited to, integrating heterogenous types of data to understand disease subtypes, causal inference to understand underlying disease mechanisms, learning from “small” labelled data, striking a balance between privacy, transparency, interpretability and model performance. This tutorial will be targeted towards a broad machine learning audience with various skill sets, some of whom may not have encountered practical applications. The main goal is to transmit inter- as well as intra- disciplinary thinking, to evaluate problems across disciplines as well as to raise awareness of context-driven solutions which can draw strength from using multiple areas of critique within the machine learning discipline. No background in healthcare or medicine is needed.

The website is available at: https://mlhealthtutorial.com/