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Project | PRECISE4Q

Personalised Medicine by Predictive Modeling in Stroke for better Quality of Life

Personalised Medicine by Predictive Modeling in Stroke for better Quality of Life

Stroke is one of the most severe medical problems with far-reaching public health and socio-economic impact, gathering momentum in an ageing society. PRECISE4Q sets out to minimise the burden of stroke for the individual and for society. It will create multi-dimensional data-driven predictive simulation computer models enabling – for the first time – personalised stroke treatment, addressing patient’s needs in four stages: prevention, acute treatment, rehabilitation and reintegration.

Heterogeneous data from multidisciplinary sources will be integrated:

  • genomics, microbiomics, biochemical;
  • imaging including mechanistic biophysiological models of brain perfusion/function;
  • social, lifestyle, gender;
  • economic and worklife,

requiring substantial efforts for information extraction, semantic labelling and standardisation.

Novel hybrid model architectures, structured prediction models, complex deep-learning and gradient boosting models will form the Digital Stroke Patient Platform including a Stroke Risk Clinical Decision Support System and coping with treatment outcomes, rehab programmes, and socio-economic planning. The decision support will be tailored to the patient's current life stage thus enabling clinicians to optimise prevention and treatment strategies over time, and will include personalised coping strategies, support of well-being and reintegration into social life and work.

The predictive capability and clinical precision will be validated with real clinical data generated by (i) prospective clinical studies and (ii) retrospective analyses of big data sets: health registries, cohort studies, health insurance data, electronic health records.

PRECISE4Q will have a clinically measurable and sustainable impact leading to better understanding of risk, health and resilience factors. In contrast to current schematic therapy guidelines, it will support patients throughout their life-long journey by personalised strategies for their specific needs.


  • Charité - Universitätsmedizin Berlin (Coordinator), Germany
  • Empirica Gesellschaft für Kommunikations- und Technologie-Forschung mbH, Germany
  • Institiuid Teicneolaiochta Bhaile Atha Cliath, Ireland
  • Eidgenössische Technische Hochschule Zürich, Switzerland
  • Tartu Ulikool, Estonia
  • Fundacio Institut Guttmann, Spain
  • Linkopings Universitet, Sweden
  • Medizinische Universität Graz, Austria
  • Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Germany
  • AOK Nordost - Die Gesundheitskasse, Germany
  • Qmenta Imaging Sl, Spain

Publications about the project

  1. MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction

    Saadullah Amin; Pasquale Minervini; David Chang; Pontus Stenetorp; Günter Neumann

    In: Proceedings of the 29th International Conference on Computational Linguistics. International Conference on Computational Linguistics (COLING-2022), located at 29th International Conference on Computational Linguistics, October 12-17, Gyeongju, Korea, Republic of, International Committee on Computational Linguistics (ICCL), 10/2022.


EU - European Union

EU - European Union