The use of data science
for the improvement of public health programs
Exploitation of individual electronic data, integration of AI algorithms, processing of heterogeneous data, Epiconcept masters these subjects which improve public health programs
The advantages of our support in Data Science
Simplifying the use of data, making it useful and enriching it while respecting the privacy of citizens requires innovative solutions and know-how that we are building with our partners.
- Detailed understanding of needs
- Mastery of technologies and standards to use the solution best suited to customer needs
- Sharing via open source and manuscripts: Epiconcept productions are made available to the scientific community.
The expertise of a multidisciplinary team
With more than 20 years of experience in health data processing, Epiconcept mobilizes multidisciplinary teams (epidemiology, R&D and Data Science) to support its customers in this process.
Public Health actors, take a step forward with us!
We support public health officials to turn a corner additional in the use of health data.
Controlled technologies / our know-how
Technologies are not an end in themselves, but we master:
- Interoperability standards eg CCAM, OMOP, CIM10.
- Open Data acquisition from repositories like Zenodo, NCBI Datasets, github or with web scrapping
- Base pairing with SNDS and HDH
- Detection of signals from social networks
- Data-visualization system with R, python or D3.js
- Integration of AI algorithms for recoding, feature extraction or text analysis.
- Processing large volumes of data with Spark and Hadoop
EpitweetR: Early Health Warning Detection Using Twitter Data
The R-based EpitweetR tool allows users to automatically monitor tweet trends based on time, location and topic, with the aim of early detection of public health threats through signals, such as an increase unusual number of tweets.
With the aim of improving screening programs, Epiconcept launched the Deep.piste study. In collaboration with the CRCDC-OC (Occitanie), the Curie Institute, and New York University (NYU), this project won the first call for projects from the Health Data Hub (HDH). The main objective of the Deep.piste study is to measure a potential improvement of the French organized breast cancer screening program (DOCS) through the use of AI.
PANDEM Pandemic Tracking Solution
The PANDEM project aims to reduce the health, socio-economic and security threats that future pandemics pose to society, so that we are better prepared for the future at regional, national, European and global levels.
Epiconcept brings its expertise in the “surveillance” lot, the specific objective of which is to identify, compile and standardize multi-source data in a coherent pandemic management database.