  With the increased availability of large databases of electronic health
records (EHRs) comes the chance of enhancing health risks screening. Most
post-marketing detections of adverse drug reaction (ADR) rely on physicians'
spontaneous reports, leading to under reporting. To take up this challenge, we
develop a scalable model to estimate the effect of multiple longitudinal
features (drug exposures) on a rare longitudinal outcome. Our procedure is
based on a conditional Poisson model also known as self-controlled case series
(SCCS). We model the intensity of outcomes using a convolution between
exposures and step functions, that are penalized using a combination of
group-Lasso and total-variation. This approach does not require the
specification of precise risk periods, and allows to study in the same model
several exposures at the same time. We illustrate the fact that this approach
improves the state-of-the-art for the estimation of the relative risks both on
simulations and on a cohort of diabetic patients, extracted from the large
French national health insurance database (SNIIRAM), a SQL database built
around medical reimbursements of more than 65 million people. This work has
been done in the context of a research partnership between Ecole Polytechnique
and CNAMTS (in charge of SNIIRAM).
