Data saves lives
What we do
In this research line, we strive to make health data Findable, Accessible, Interoperable and Reusable (FAIR). We develop and apply methods and policies regarding the input, storage and utilization of health data and knowledge. Additionally, we make agreements for when the data is understandable. One of our focus areas is the care for individuals with rare diseases.
Scientific projects
C4C
c4c (conect4children) is a European network that facilitate the development of new drugs and other therapies for the entire paediatric population. It is a pioneering opportunity to build capacity for the implementation of multinational paediatric clinical trials whilst ensuring the needs of babies, children, young people and their families are met.
ERDERA
The majority of rare and ultra rare diseases still lack a therapeutic option. ERDERA will continue the development of a robust and comprehensive data and expertise infrastructure and innovative clinical research services, funding new research projects, providing training and expediting translation of findings into solutions for patients.
KIK Staff: Nirupama Benis, Ronald Cornet, Andra Waagmeester, Noah van Brummelen
HemaFAIR
The HemaFAIR project aims to enhance the scientific profile of The Cyprus Institute of Neurology and Genetics and improve the position of Cypriot researchers in biomedical informatics and rare hematological diseases with a focus on the use of FAIR data and standards, including a comprehensive training programme.
KIK Staff: Ronald Cornet, Martijn Kersloot
LEARNFAIR
The LEARN-FAIR project intends to foster cooperation and knowledge exchange among FAIR (Findable, Accessible, Interoperable, and Reusable data) trainers by establishing a Dutch FAIR Trainers Community. The project focuses on identifying training needs and existing educational materials, to develop new Open Educational Resources.
KIK Staff: Ronald Cornet, Martijn Kersloot, Myrthe van Heerde
NICE Federated Learning
This project tests the feasibility of Federated Learning, a decentralized machine learning technique. We will examine the performance of NICE’s yearly APACHE IV recalibration when calculated centralized versus decentralized (recreated virtually) and investigate its effect on the position of ICUs in funnel plots.
KIK Staff: Ferishta Raiez, Sebastian van der Voort, Ronald Cornet, Nicolette de Keizer
PaLaDIn
PaLaDIN aims to accelerate the development of effective treatments and to establish best-practice diagnosis and care for neuromuscular disease patients. PaLaDIn will develop and operate a collaborative, inclusive system that collects patient-reported outcome and experience measures, and aligns these with clinical data.
KIK Staff: Nirupama Benis, Ronald Cornet, Martijn Kersloot, Lilli Schuckert