Secure ETL pipelines, SNDS extraction and linearisation, machine learning models and clinical natural language processing.
We build robust, scalable and secure pipelines to extract, transform and analyse health data.
Proprietary ETL pipeline in Spark/Scala. 3 steps: SNDS to Parquet conversion, cleaning via WikiPipeline, entity extraction (CCAM, ICD-10, LPP, Drugs, Patients, Consultations, ALD, GHM). V2 medico-economic module for care pathway valuation.
11 specialised Spark/Scala jobs: care pathway variables, hospitalisations, drugs, DDD exposure, polypharmacy, resource consumption, sick leave and costs. 100% test coverage.
Classification, regression and clustering algorithms. Automatic phenotyping of complex conditions using deep learning.
Automatic anomaly detection in datasets. Business rule validation, temporal consistency and variable completeness checks.
Automated quality analysis of registry data before processing.
Common engine with core functions plus registry-specific functions. Developed in Python with test coverage.
Supports Excel files and specification files. Produces a normalised registry database (1 row per patient, 1 column per raw variable).
Docker containerisation, GitLab versioning, continuous integration.
Our platform is built on an HDS-certified infrastructure, with isolated environments and end-to-end encryption.
Infrastructure compliant with health data security standards, annual audit and continuous certification.
Dedicated workspaces per project, access segregation, complete audit logs for full traceability.
Data encrypted in transit and at rest. Encryption key management, multi-factor authentication for all access.
Real-time pipeline monitoring, anomaly alerts, performance and uptime dashboards.
We integrate artificial intelligence across all our processes to improve production timelines and the quality of our deliverables.
AI-assisted writing of study protocols, literature review and structuring of CESREES applications. Significant time savings on preparatory phases.
AI-assisted code generation and review for ETL pipelines, analysis scripts and phenotyping algorithms. Reduced errors and faster development.
AI assistance for interpreting statistical results, detecting anomalies in datasets and identifying patterns in patient care pathways.
Automated consistency checking of results, outlier detection, cross-validation of outputs. AI reinforces every step of our production chain.
Discover our technical infrastructure and data science capabilities.
We build robust and scalable pipelines for your health data.