Machine Learning Methodologies for Personalized Health



It is now possible to have several perspectives on a patient. mHealth provides information derived from mobile phones. Full genotyping of patients is becoming affordable, providing information about genetic background. The phenotype of disease is becoming better characterised than ever before. Techniques such as transcriptome analysis allow a highly detailed characterization of the state of a tissue. Finally the UK government’s midata initiative (and similar initiatives elsewhere) may eventually allow patients to provide information about their consumer spending habits as well as social network behaviour.\ \ These different data modalities need to be combined into one model of the patients well being. There are major challenges with doing this: models need to be applied across millions of patients and for any given patient many information modalities will be missing. Addressing data of this type requires new machine learning methodologies. This project will focus on combining data from different modalities within the same probabilistic model.