Volv Global Poster: Early prediction of ARDS in community-acquired pneumonia patients using machine learning
Presenting new research on whether a machine learning model can flag which patients with CAP are most likely to progr...
Volv Global personalises healthcare by generating insight into patients and diseases, powered by data-driven AI and machine learning.
Our proprietary machine learning methodologies serve pharmaceutical companies, reducing costs across the healthcare ecosystem and improving outcomes for people living with disease.
Volv Global tailors diagnosis, treatment and care pathways to each patient's own data, replacing generic assumptions with the precision and personalisation their specific case actually needs.
Volv Global's methodology characterises and segments patient populations, giving healthcare organisations and patients clearer pathways, and pharmaceutical teams more efficient development and commercialisation.
Volv Global generates new understanding of patient biomarkers, phenotypes, clinical features and endpoints, helping clinicians see how a disease presents.
Late and incorrect diagnosis carries an avoidable cost across the system, from unnecessary testing to delayed care. Volv Global's methodology helps payers and providers lower that burden and improve outcomes.
Insight that supports smarter decisions
Volv Global uses AI and real-world data to generate knowledge about difficult-to-diagnose diseases.
Clients use these insights to strengthen clinical development strategy, deepen disease understanding, and find opportunities to develop diagnostic tools faster.
The same insights support repositioning existing drugs to extend their R&D viability, helping clients reach new markets and work smarter.
Volv Global's methodology powers outcomes across stakeholders
De-risk clinical strategy, strengthening trial planning around a clearer patient picture.
Generate real-world evidence, supporting pricing and reimbursement decisions.
Expand medical understanding with new real-world data insight into disease progression and outcomes.
Support a strong launch and extend the product lifecycle by finding more patients sooner.
Innovating for patients that routine care overlooks
Volv Global combines AI with real-world data to deliver solutions for people with rare and difficult-to-diagnose diseases. Its methodology surfaces patients whose data is consistent with detected patterns, generates real-world evidence for clinical trials and regulatory submissions, optimises the patient journey with data-driven insight, and reduces time and cost in drug development and market access.
Pharmaceutical innovators, healthcare providers and policymakers working with Volv Global gain the tools to make faster, better-supported decisions across the healthcare ecosystem.
Volv Global, in numbers
Introducing our methodology
Volv Global’s proprietary methodology finds the patients a health system cannot yet see, flags patients for HCP-led review before conventional symptoms may appear, predicts what happens next, and recognises who is eligible for treatment now. It can be adapted to any difficult-to-diagnose disease, helping clinicians reach people sooner.
Understanding how each disease presents, patient by patient
Grow the pool of real-world evidence on a disease, giving healthcare teams a clearer view of who is affected.
Surface subtle patterns among biomarkers, recognising patients on a trajectory towards a condition sometimes years before conventional signs appear.
Draw on real-world evidence to model disease progression for patients already diagnosed, anticipating response and outcomes clinicians can plan around.
Find patients who are already diagnosed but not yet on treatment, closing the gap between diagnosis and care for healthcare organisations and patients alike.
The human story
Volv Global gives pharmaceutical innovators insight into difficult-to-diagnose diseases, so they can develop effective technology and treatments.
For the patient, this can mean an earlier diagnosis, a life-changing drug, or the chance to connect with a wider community for education and support while living with a rare disease.
Stay informed
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Presenting new research on whether a machine learning model can flag which patients with CAP are most likely to progr...
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