Titre :

LinkR: An open source, low-code and collaborative data science platform for healthcare data analysis and visualization.

Auteur(s) :

Delange, B. [Auteur]
Laboratoire de Traitement du Signal et de l'Image [LTSI]
Popoff, B. [Auteur]
CHU Rouen
Séité, T. [Auteur]
Centre hospitalier Saint-Brieuc Paimpol Tréguier [CH Saint-Brieuc Paimpol Tréguier]
Lamer, Antoine [Auteur]
Fédération Régionale de Recherche en Psychiatrie et santé mentale d’Occitanie [FERREPSY Occitanie]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Parrot, A. [Auteur]

Titre de la revue :

Int J Med Inform

Nom court de la revue :

Int J Med Inform

Numéro :

199

Pagination :

105876

Date de publication :

2025-04-05

ISSN :

1872-8243

Mot(s)-clé(s) en anglais :

Clinical data warehouse
Electronic health records
Interoperability
Web application
Data visualization
Data analysis
Low-code data analysis

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Background The development of Clinical Data Warehouses (CDWs) has greatly increased access to big data in medical research. However, the lack of standardization among different data models hampers interoperability and, ...
Lire la suite >Background The development of Clinical Data Warehouses (CDWs) has greatly increased access to big data in medical research. However, the lack of standardization among different data models hampers interoperability and, consequently, the research potential of these vast data resources. Moreover, data manipulation and analysis require advanced programming skills, a skill set that healthcare professionals often lack. Methods To address these issues, we created an open source, low-code and collaborative data science platform for manipulating, visualizing and analyzing healthcare data using graphical tools alongside an advanced programming interface. The software is based on the OMOP Common Data Model. Results LinkR enables users to generate studies using data imported from multiple sources. The software organizes the studies into two main sections: individual and population data sections. In the individual data section, user-friendly graphical tools allow users to customize data presentation, recreating the equivalent of a medical record, according to the needs of their study. The population data section is designed for conducting statistical analyses through both graphical and programming interfaces. The application also integrates a Git module, streamlining collaboration and facilitating shared data analysis across research centers. The platform was tested with datasets including the OMOP database (46,520 patients and over 36 million rows in the measurement table) during the InterHop datathon with 12 concurrent users. Usability testing yielded a median System Usability Scale (SUS) score of 75 [63.8–85.6], indicating high user satisfaction. Conclusion LinkR is a low-code data science platform that democratizes access, manipulation, and analysis of data from clinical data warehouses and facilitates collaborative work on healthcare data, using an open science approach.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2025-04-08T21:04:15Z
2025-04-16T08:12:20Z