Industrial partners
Accelerate your research projects using real-life data with our partner establishments.
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Get in touch with our healthcare partners organizations

We support industry players by identifying the most relevant sources of real-life data to carry out their studies with our network of partner healthcare establishments. 

We bring together manufacturers and healthcare organisations to better research and improve patient care. 

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RARE DISEASES

Develop an algorithm for identifying patients experiencing diagnostic wandering in collaboration with one of our partner centres.
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REAL WORLD EVIDENCE

Quickly identify partner centres to carry out your real-life studies
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RARE DISEASES

Rare diseases: identify patients in a situation of diagnostic wandering faster

Fernandes

Philippe Fernandes, PhD

Pharma Key Account Manager
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Use our algorithms to identify patients with phenotypic similarity

Using real-world data, we have created AI-driven tools that not only detect rare diseases but also produce evidence to advance medical research.

Developed at the Imagine Institut, codoc's sophisticated search engine processes unstructured data within patient records to pinpoint individuals who may be at risk for rare diseases.

This system uses natural language processing (NLP) to extract phenotypic concepts from clinical documents to calculate patient similarity scores. This model identifies patient profiles exhibiting clinical similarities to the studied rare disease. 

This phenotype-based pre-selection strategy, validated through the clinical data warehouse at Necker-Enfants Malades pediatric hospital, successfully identified patients with various diseases.

This approach and its outcomes have been documented and published in peer-reviewed journals.
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Related publications :

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REAL WORLD EVIDENCE

Fast access to unstructured data for retrospective studies

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Members of the codoc network can respond torequests for retrospective studies based on real-world data in just a few months.
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The codoc solution incorporates a natural language processing (NLP)pipeline that enhances:
  • The richness and quality of data in our partner institutions' data warehouses
  • The precision of search queries to identify relevant data.

80%

medical information is present in unstructured data *

*Escudié J-B et al. A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease. BMC Med Inform Decis Mak. 2017

Our Collaboration Process

Codoc will connect you with one of our partner institutions and support you throughout your retrospective study.
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01

Pre-feasibility

Screen eligible patients in our healthcare partner organizations

02

Feasibility

More detailed eligibility criteria and report for the study

03

Availability

Data provision in secure workspaces thatcomply with CNIL regulations

04

Deliverables

Statistical data analysis and study report
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All our solutions are designed to comply with the CNIL's standards, guaranteeing compliance with the formalities required by the administrative authority throughout the value chain.

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