HeKA is a common project-team of Inria, Inserm and Université de Paris. HeKA is affiliated with the “Centre de Recherche des Cordeliers” and Inria Paris.

Hospital information systems are used at every step of patient care, collecting continuously longitudinal data, both unstructured and structured, including clinical reports, drug prescriptions, laboratory results and omics data. Unfortunately, the knowledge that can be acquired from previously collected health data is hardly considered in the clinical care of new patients.

The objective of HeKA is to develop methodologies, tools and their applications in clinics towards a learning health system, i.e., a health system that leverages clinical data collected to extract agilely and reliably novel medical knowledge that, in turn, continuously improves healthcare. We rely on the availability of EHRs (Electronic Health Records), clinical trials, cohorts and other linked data to develop models for stratification and prediction with the potential of improving the precision and the personalization of treatments, and in turn the quality of healthcare.

With this objective, HeKA research activity follows 3 interdependent axes: (1) Patient phenotyping and representation learning, (2) Stochastic and data-driven predictive models for decision guiding, and (3) Designs of next generation clinical trials.

Keywords: Data-driven medicine, Model-based medicine, Learning health system, Precision medicine, Knowledge acquisition, Representation learning, Predictive modelling, Next generation clinical trials, Small samples, Translational research, Electronic Health Records, Machine learning, Bayesian inference .

HeKA is both a reference of the Egyptian godess of medicine and a acronym for Health data- and model- driven Knowledge Acquisition. HeKA is the follow-up of the Team 22 (Information Sciences to support Personalized Medicine) led by Prof. Anita Burgun at the “Centre de Recherche des Corderliers” (Inserm, Université de Paris).

Research directions

  • Axis 1: Knowledge extraction and discovery from healthcare data
  • Axis 2: Stochastic and supervised learning for clinical decision support
  • Axis 3: Data-driven and designs for next generation clinical trials

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