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| Funder | European Commission |
|---|---|
| Recipient Organization | Academisch Ziekenhuis Leiden |
| Country | Netherlands |
| Start Date | May 01, 2023 |
| End Date | Apr 30, 2028 |
| Duration | 1,826 days |
| Number of Grantees | 24 |
| Roles | Coordinator; Participant; Associated Partner; Third Party |
| Data Source | European Commission |
| Grant ID | 101080711 |
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 decisionTo 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.
Academisch Ziekenhuis Leiden; Iqvia Solutions B.V.; The Academic Health Science Network for the North East and North Cumbria Limited; Philipps Universitaet Marburg; Friedrich-Alexander-Universitaet Erlangen-Nuernberg; Fundacion Para la Investigacion E Innovacion Biosanitaria de Atencion Primaria; The University of Manchester; Instituto de Salud Musculoesqueletica Sl; Fundacion Para la Investigacion Biomedica Del Hospital Universitario Clinico San Carlos; Medizinische Hochschule Brandenburg Campus Gmbh; University of Newcastle Upon Tyne; Fundacion Para la Investigacion Biomedica Del Hospital Universitario la Princesa; Servicio Madrileno de Salud; Phadia Gmbh; Idryma Iatroviologikon Ereunon Akademias Athinon; Elsa Science Ab; Fundacion Para la Investigacion Biomedica Del Hospital Gregorio Maranon; Semmelweis Egyetem; Karolinska Institutet; Fundacion Para la Investigacion Biomedica Del Hospital Universiatrio la Paz; Erasmus Universitair Medisch Centrum Rotterdam; Universitatsklinikum Erlangen; Stichting Reumanederland; Technische Universiteit Delft
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