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Stratification of Patients using advanced Integrative modeling of Data Routinely acquired for diagnosing Rheumatic complaints

Project description

Stratification of patients with musculoskeletal symptoms using advanced integrative data modelling

Early disease stratification is important to ensure appropriate care of patients with musculoskeletal symptoms. The EU-funded SPIDeRR project aims to streamline early rheumatic diseases diagnosis. The innovative approach will identify disease groups amongst similar-symptom patients by integrating all relevant data dimensions from every healthcare level. Application of machine learning techniques from the omics field to clinical patient data will result in new pipelines for translational science. The project objective is to deliver three clinical models: a symptom checker for patients; a support tool for healthcare providers, guiding additional examination and referrals; and a patient comparison network for optimisation of diagnostic groups and treatment decisions.

Objective

Globally 1.Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, trial and error approach in initial treatment.

SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients symptoms. SPIDeRRs approach will go well beyond the state-of-the-art in the following ways:
- By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early.
- By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online).
- By translating and applying machine learning techniques from the omics field to clinical patient data, which will result in new pipelines for translational data science

SPIDERR will deliver three clinical models
-a symptom checker for patients
-a decision support tool for (primary) care providers providing guiding additional examination and referral decisions
-a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision

To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines.
Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.

Fields of science (EuroSciVoc)

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Topic(s)

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-HLTH-2022-TOOL-12-two-stage

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Coordinator

ACADEMISCH ZIEKENHUIS LEIDEN
Net EU contribution

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.

€ 1 171 050,00
Address
ALBINUSDREEF 2
2333 ZA Leiden
Netherlands

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Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 1 171 050,00

Participants (19)

Partners (4)

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