Artificial intelligence for the evaluation of profiles, trajectories and management of vulnerability in health: COVID-19, frailty of the elderly and cancer
HEKA (Project Team)
Health data- and model- driven Knowledge Acquisition
Underway
Collaboration type
Research structure
Main objectives
HeKA is a joint research project-team of Inria, Inserm and the University of Paris. HeKA depends on the Cordeliers Research Center and the Inria Center in Paris.
HeKA is composed of researchers, hospital-universities and teacher-researchers from Inria, Inserm, the University of Paris and AP-HP, associated with services of the Georges Pompidou European Hospital, the Necker Hospital and the Imagine Institute.
The research themes of the team are medical informatics, biostatistics and applied mathematics for clinical decision support.
The shared objective of the team is the development of methods, models and tools for a learning health system. This paradigm, which we are developing in particular for rare diseases in the C'IL-LICO RHU, takes advantage of the data generated during care to learn new knowledge, which in turn is used to guide clinical practice, on an ongoing basis. To achieve this goal, HeKA focuses on 3 closely related research areas:
(1) knowledge extraction from health data and in particular deep phenotyping ;
(2) stochastic and supervised approaches for decision support
(3) future clinical trials and their design, which allow the evaluation of medical decision support systems.
HeKA is composed of researchers, hospital-universities and teacher-researchers from Inria, Inserm, the University of Paris and AP-HP, associated with services of the Georges Pompidou European Hospital, the Necker Hospital and the Imagine Institute.
The research themes of the team are medical informatics, biostatistics and applied mathematics for clinical decision support.
The shared objective of the team is the development of methods, models and tools for a learning health system. This paradigm, which we are developing in particular for rare diseases in the C'IL-LICO RHU, takes advantage of the data generated during care to learn new knowledge, which in turn is used to guide clinical practice, on an ongoing basis. To achieve this goal, HeKA focuses on 3 closely related research areas:
(1) knowledge extraction from health data and in particular deep phenotyping ;
(2) stochastic and supervised approaches for decision support
(3) future clinical trials and their design, which allow the evaluation of medical decision support systems.
#AideALaDécisionClinique | #AppliedMathematics | #ApprochesStochastiques | #ApprochesSupervisées | #BioStatistics | #Biostatistiques | #ClinicalDecisionSupport | #ClinicalTrialsOfTheFuture | #EssaisCliniquesduFutur | #InformatiqueMédicale | #MathématiquesAppliquées | #MedicalInformatics | #Phénotypage | #Phenotyping | #StochasticMethods | #SupervisedMethods

Members
Inria, Inserm, Université de Paris, AP-HP, Institut Imagine
Useful links
https://team.inria.fr/heka/
Other projects
Description
Learning a deep representation of patient records for event prediction and patient segmentation
Names of partners involved
AP-HP, Inria