Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research.

big data cancer cancer research data mining data warehouse natural language processing

Journal

International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455

Informations de publication

Date de publication:
07 Feb 2024
Historique:
received: 15 11 2023
revised: 28 01 2024
accepted: 31 01 2024
medline: 24 2 2024
pubmed: 24 2 2024
entrez: 24 2 2024
Statut: epublish

Résumé

Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects. UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals. Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers. Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.

Sections du résumé

BACKGROUND BACKGROUND
Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects.
METHODS METHODS
UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals.
RESULTS RESULTS
Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers.
CONCLUSIONS CONCLUSIONS
Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.

Identifiants

pubmed: 38397680
pii: ijerph21020189
doi: 10.3390/ijerph21020189
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Agence Nationale de la Recherche
ID : ANR-10-EQPX-03
Organisme : French National Cancer Institute
ID : Inca-DGOS-4654
Organisme : French National Cancer Institute
ID : INCa-DGOS-Inserm_12554

Auteurs

Julien Guérin (J)

Institut Curie, 75005 Paris, France.

Amine Nahid (A)

Coexya, 69370 Saint-Didier-au-Mont-d'Or, France.

Louis Tassy (L)

Institut Paoli-Calmettes, 13009 Marseille, France.

Marc Deloger (M)

Gustave Roussy, 94805 Villejuif, France.

François Bocquet (F)

Data Factory & Analytics Department, Institut de Cancérologie de l'Ouest, 44805 Nantes-Angers, France.

Simon Thézenas (S)

Institut Régional du Cancer de Montpellier, 34090 Montpellier, France.

Emmanuel Desandes (E)

Institut de Cancérologie de Lorraine, 54519 Nancy, France.

Marie-Cécile Le Deley (MC)

Centre Oscar Lambret, 59000 Lille, France.

Xavier Durando (X)

Centre Jean Perrin, 63011 Clermont Ferrand, France.

Anne Jaffré (A)

Institut Bergonié, 33076 Bordeaux, France.

Ikram Es-Saad (I)

Centre Georges Francois Leclerc, 21000 Dijon, France.

Hugo Crochet (H)

Centre Léon Bérard, 69008 Lyon, France.

Marie Le Morvan (M)

Institut Paoli-Calmettes, 13009 Marseille, France.

François Lion (F)

Gustave Roussy, 94805 Villejuif, France.

Judith Raimbourg (J)

Data Factory & Analytics Department, Institut de Cancérologie de l'Ouest, 44805 Nantes-Angers, France.

Oussama Khay (O)

Institut de Cancérologie de Lorraine, 54519 Nancy, France.

Franck Craynest (F)

Centre Oscar Lambret, 59000 Lille, France.

Alexia Giro (A)

Centre Jean Perrin, 63011 Clermont Ferrand, France.

Yec'han Laizet (Y)

Institut Bergonié, 33076 Bordeaux, France.

Aurélie Bertaut (A)

Centre Georges Francois Leclerc, 21000 Dijon, France.

Frederik Joly (F)

Coexya, 69370 Saint-Didier-au-Mont-d'Or, France.

Alain Livartowski (A)

Institut Curie, 75005 Paris, France.

Pierre Heudel (P)

Centre Léon Bérard, 69008 Lyon, France.

Classifications MeSH