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

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

  • Duration:

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

Dominik Stammbach, Günter Neumann

In: Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER). Workshop on Fact Extraction and Verification (FEVER-2019) located at EMNLP-IJCNLP 2019 November 3 Hong Kong China Pages 105-109 Association for Computational Linguistics 11/2019.

To the publication
Saadullah Amin, Günter Neumann, Katherine Dunfield, Anna Vechkaeva, Kathryn Annette Chapman, Morgan Kelly Wixted

In: CLEF 2019 Working Notes. Conference and Labs of the Evaluation Forum (CLEF-2019) 10th Conference and Labs of the Evaluation Forum September 9-12 Lugano Switzerland 9/2019.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz