  Managing patients with chronic diseases is a major and growing healthcare
challenge in several countries. A chronic condition, such as diabetes, is an
illness that lasts a long time and does not go away, and often leads to the
patient's health gradually getting worse. While recent works involve raw
electronic health record (EHR) from hospitals, this work uses only financial
records from health plan providers (medical claims) to predict diabetes disease
evolution with a self-attentive recurrent neural network. The use of financial
data is due to the possibility of being an interface to international
standards, as the records standard encodes medical procedures. The main goal
was to assess high risk diabetics, so we predict records related to diabetes
acute complications such as amputations and debridements, revascularization and
hemodialysis. Our work succeeds to anticipate complications between 60 to 240
days with an area under ROC curve ranging from 0.81 to 0.94. In this paper we
describe the first half of a work-in-progress developed within a health plan
provider with ROC curve ranging from 0.81 to 0.83. This assessment will give
healthcare providers the chance to intervene earlier and head off
hospitalizations. We are aiming to deliver personalized predictions and
personalized recommendations to individual patients, with the goal of improving
outcomes and reducing costs
