SCALPEL3: A scalable open-source library for healthcare claims databases.

Objective: This article introduces SCALPEL3 (Scalable Pipeline for Health Data), a scalable open-source framework for studies involving Large Observational Databases (LODs). It focuses on scalable medical concept extraction, easy interactive analysis, and helpers for data flow analysis to accelerate studies performed on LODs.

Materials And Methods: Inspired from web analytics, SCALPEL3 relies on distributed computing, data denormalization and columnar storage.

ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection.

With the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies 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.