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The research in this project is focused on overcoming the main technical and scientific challenges that hinder the full deployment of clinical decision support systems in oncological health care: these systems are currently still difficult to maintain, lack explanatory power, and are weakly integrated in the clinical workflow (both technically and professionally). There is a lack of attention for the impact on patient well‐being and HRQoL aspects of treatment and follow‐up procedures, and we do not yet know well how to deal well with personalized data (including consequences of treatment choices, interference and co-morbidities, and longitudinal patient data). We organized our research projects along three research lines: Reasoning under Uncertainty, Clinical Data Science, Hybrid Intelligence, and Clinical Modelling.


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Reasoning under Uncertainity

Three work packages focusing on justification and explanation, maintenance and online learning of drifting and evolvable data, and robust and trustworthy advice in Bayesian networks).

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Clinical Data Science

Two work packages focusing on co-occurring events in personalized predictions and on causal discovery methods for personalized assessment.

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Hybrid Intelligence

Three work packages focusing on the integration of clinical decision support in the clinic, hybrid AI for trustworthy explanations, and collecting and using patient-reported measures as input for AI models.

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Clinical Modelling

Three demonstrators focusing on the development, refinement, and deployment of clinical models for lung, prostate, stomach, and endometrial cancer.