Objective
Background: Artificial intelligence (AI) holds great promise for improving patient care, but challenges related to data irregularity and complexity have hindered its translation into clinical practice. Modelling rich longitudinal electronic health records (EHRs) such as those found in intensive care units (ICUs) remains especially difficult, as they represent a complex interplay between the patient’s health and clinical decisions made in response.
Objectives: We aim to develop a robust AI framework for flexible prediction of any outcome in the ICU and beyond. We will pioneer a class of generative pre-trained models optimised for complex EHR data (Objective 1). Our approach will be rigorously benchmarked across outcomes and hospitals (Objective 2) on a secure, federated infrastructure that ensures data privacy (Objective 3).
Methods: Our approach treats EHR data as a stream of clinical events in continuous time and uses generative pre-training to learn both the likely time and the content of the next event. From generated sequences of future events, we will derive explainable predictions for any clinical outcome, including those not encountered before (zero-shot learning). Our approach will be powered by the largest set of harmonised multicentre ICU data to date, covering up to 1 million patients with 33 billion clinical events. We will refine existing methods in federated learning to allow for secure decentralised training of our models at scale.
Innovation & Impact: Our work will introduce a novel paradigm for AI-based clinical risk prediction, setting the stage for a new era of flexible, general-purpose AI in medicine. Our unprecedented multicentre benchmarks will provide an urgently needed baseline for meaningful innovations in the field, while our federated approach will facilitate secure model building across institutions and borders.
Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
6020 INNSBRUCK
Austria
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.