title: Prediction of health outcomes using big (health) data.
abstract: The vast amounts of information in the form of electronic medical records are used to develop a novel model of disease progression. The proposed model is based on the representation of a patient's medical history in the form of a binary history vector, motivated by empirical evidence from previous work and validated using a large `real-world' data corpus. The scope for the use of the described methodology is overarching and ranges from smarter allocation of resources and discovery of novel disease progression patterns and interactions, to incentivization of patients to make lifestyle changes.
authors.forename: Ognjen
authors.initials: O
authors.lastname: Arandjelovic
mesh_terms: D016208:Databases, Factual; D042241:Early Diagnosis; D057286:Electronic Health Records; D036262:Empirical Research; D006801:Humans; D008962:Models, Theoretical; D015203:Reproducibility of Results