Enabling integration of medical and research data, secure data sharing and leveraging responsible state-of-the-art artificial intelligence (AI)-mediated models opens immense possibilities to mitigate the impact of chronic immune-mediated diseases (CIMDs) affecting 10% of Europeans. Eight European universities, leaders in the medical and analytical field, three SMEs, one research institute and one company, at the forefront of clinical AI implementation, data infrastructure, and security, and a Patient Organisation formed the consortium WISDOM. The consortium's overarching aim is to convert complex biological information from the existing data sources into actionable insights. WISDOM builds on the premise that computational tools can provide valuable knowledge and guide decision-making at critical stages in the individual patient journey, from diagnosis to treatment initiation and optimisation. To unlock the potential of the existing data, WISDOM will address barriers of data integration and accessibility and deploy novel approaches for data processing, harmonisation, integration, and secure, trustworthy data sharing with federated access. WISDOM aims to develop computational risk stratification and outcome prediction models and tools in different CIMD use cases, building on large EU-funded multimodal datasets, and prospectively validate them on technical, clinical and user aspects to facilitate data-driven and patient-focused diagnosis, treatment, and monitoring. WISDOM aims to promote the widespread utilisation of data and facilitate responsible and critical assessment of the use of AI in healthcare using an end-user guided approach leveraging collaboration among clinicians, researchers, legal and AI experts, patient associations and rich stakeholder expertise. WISDOM’s ultimate ambition is to revolutionise the integration, management, and analysis of health data across diseases and borders to promote personalised interventions and well-being.

In 2013, cirrhosis was responsible for 1.2 million deaths worldwide. This mortality is mainly due to cirrhosis decompensation, i.e. development of ascites, hepatic encephalopathy, and/or gastrointestinal hemorrhage, and its progression to acute-on-chronic liver failure (ACLF). Patients with decompensated cirrhosis receive many treatments such as intravenous and oral absorbable antibiotics, oral non-absorbable antibiotics, albumin, proton-pump inhibitors, laxatives, diuretics, betablockers, vasoconstrictors, statins, anticoagulants, steroids and antiviral agents. Despite these multiple treatments, ACLF or mortality in patients with decompensation of cirrhosis remains high (15% at day 28, 28% at day 90) because of large interindividual variability in precipitating events, in clinical presentation and in response to treatment. This heterogeneity calls for treatment personalization according to underlying mechanisms. The objective of DECISION is to enhance our understanding, at systems level, of the pathophysiology of decompensation of cirrhosis leading to ACLF or death to decrease patients’ mortality at day 28. First, DECISION will improve our knowledge of the pathophysiology of decompensation of cirrhosis by integrating results of high-throughput multi-omic profiling with comprehensive clinical data from 2,200 fully characterized patients (more than 8,600 time points) with available standardized biological samples. Second, we will identify novel combinatorial therapies for patients with decompensation of cirrhosis to prevent death. We will refine these therapies in new and/or optimized animal models and then test the best combination in high risk patients in a phase II clinical trial built in DECISION. Third, we will develop 2 tests: one predicting outcome of patients with decompensation of cirrhosis when treated with standard treatment (prognostic test); and the other identifying patients who will respond to the novel combinatorial therapy (test for response).

The complex interactions between genetic and non-genetic factors produce heterogeneities in patients as reflected in the diversity of pathophysiology, clinical manifestations, response to therapies, disease development and progression. Yet, the full potential of personalized medicine entails biomarker-guided delivery of efficient therapies in stratified patient populations. MultipleMS will therefore develop, validate, and exploit methods for patient stratification in Multiple Sclerosis, a chronic inflammatory disease and a leading causes of non-traumatic disability in young adults, with an estimated cost of €37 000 per patient per year over a duration of 30 years. Here we benefit from several large clinical cohorts with multiple data types, including genetic and lifestyle information. This in combination with publically available multi-omics maps enables us to identify biomarkers of the clinical course and the response to existing therapies in a real-world setting, and to gain in-depth knowledge of distinct pathogenic pathways setting the stage for development of new interventions. To create strategic global synergies, MultipleMS includes 21 partners and covers not only the necessary clinical, biological, and computational expertise, but also includes six industry partners ensuring dissemination and exploitation of the methods and clinical decision support system. Moreover, the pharmaceutical industry partners provide expertise to ensure optimal selection and validation of clinically relevant biomarkers and new targets. Our conceptual personalized approach can readily be adapted to other immune-mediated diseases with a complex gene-lifestyle background and broad clinical spectrum with heterogeneity in treatment response. MultipleMS therefore goes significantly beyond current state-of-the-art thereby broadly affecting European policies, healthcare systems, innovation in translating big data and basic research into evidence-based personalized clinical applications.