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Shared decision making about personalised care

PersOn consortium


Samen beslissen over gepersonaliseerde zorg

Ieder jaar krijgen meer mensen de diagnose kanker. Vaak wacht hen een standaardbehandeling die geen rekening houdt met de verwachtingen van de patiënt over de resulterende kwaliteit van leven. Het PersOn-programma analyseert met behulp van kunstmatige intelligentie zowel de beschikbare medische gegevens over behandeling en nazorg en de mogelijke impact op kwaliteit van leven zodat zorgverlener en patiënt samen kunnen beslissen over het individuele zorgtraject.


Shared decision making about personalised care 

More people are diagnosed with cancer every year. Often they will receive a standard treatment that does not pay attention to the patient’s expectations and wishes about the quality of the remainder of their life. The PersOn program analyses, using artificial intelligence, both the clinical information on treatment and follow-up as well as the possible impact on the patient’s quality of life, such that patient and health care practitioner can make a shared decision about personalised care.



This consortium is part of the project PersOn (with project number P21-03) of the research programme Perspectief 2021-2022 2022 TTW which is financed by the Dutch Research Council (NWO) domain Applied and Engineering Sciences. Link in English and Dutch.

Societal & Technical Challenges

In health care today, clinical decision support systems are AI systems that combine medical expertise with clinical data sets in a statistical computer model. While the specific purpose and challenges differ per oncological domain and phase in health care (i.e., diagnosis, treatment, and follow‐up), with the data that oncologists enter into a medical record (i.e., a patient’s age, genetics, cancer staging and associated medical problems), a computer can establish the most likely diagnosis, best possible treatment, or most appropriate follow-up policy. There are, however, several important shortcomings that limit the use of these systems in the clinic: they are difficult to maintain, lack explanatory power, and are weakly integrated in the clinical workflow (both technically and professionally). These shortcomings, together with the lack of attention for (and data of) the impact on patient well‐being and HRQoL aspects of treatment and follow‐up procedures, are the major obstacles that prevent AI-assisted shared decision making about personalized oncological care that are addressed in this programme.


Our programme addresses the crucial societal challenge to offer high-quality, personalised oncological care despite an increasing number of patients by developing and integrating state-of-the-art AI technology for extracting statistical medical knowledge, developing clinical models, and offering personalised care with novel insights in explanation and justification of AI-generated advice in CDSSs. We integrate knowledge and expertise in AI and data science, clinical modelling, user-based design, and medical technology in a consortium that brings together all relevant stakeholders necessary to tackle this important challenge. Each partner in the consortium brings a unique set of expertise and knowledge to make this programme a success.

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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.

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