Papers › SCALPEL3: a scalable open-source library for healthcare claims databases
SCALPEL3: a scalable open-source library for healthcare claims databases
Emmanuel Bacry, Stéphane Gaïffas, Fanny Leroy, Maryan Morel, Dinh Phong Nguyen, Youcef Sebiat, Dian Sun
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
This article introduces SCALPEL3, a scalable open-source framework for studies involving Large Observational Databases (LODs). Its design eases medical observational studies thanks to abstractions allowing concept extraction, high-level cohort manipulation, and production of data formats compatible with machine learning libraries. SCALPEL3 has successfully been used on the SNDS database (see Tuppin et al. (2017)), a huge healthcare claims database that handles the reimbursement of almost all French citizens. SCALPEL3 focuses on scalability, easy interactive analysis and helpers for data flow analysis to accelerate studies performed on LODs. It consists of three open-source libraries based on Apache Spark. SCALPEL-Flattening allows denormalization of the LOD (only SNDS for now) by joining tables sequentially in a big table. SCALPEL-Extraction provides fast concept extraction from a big table such as the one produced by SCALPEL-Flattening. Finally, SCALPEL-Analysis allows interactive cohort manipulations, monitoring statistics of cohort flows and building datasets to be used with machine learning libraries. The first two provide a Scala API while the last one provides a Python API that can be used in an interactive environment. Our code is available on GitHub. SCALPEL3 allowed to extract successfully complex concepts for studies such as Morel et al (2017) or studies with 14.5 million patients observed over three years (corresponding to more than 15 billion healthcare events and roughly 15 TeraBytes of data) in less than 49 minutes on a small 15 nodes HDFS cluster. SCALPEL3 provides a sharp interactive control of data processing through legible code, which helps to build studies with full reproducibility, leading to improved maintainability and audit of studies performed on LODs.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections