Building on Volv Global`s inTrigue capability, inClude is an advanced way of building computational models using machine learning to detect difficult to diagnose, and reveal the degree of heterogeneity, at the level of patients phenotypes and predictable course of disease, in population-scale databases such as electronic health records.
With at the heart of it, the core predictive modeling make up of the inTrigue methodology, inClude is designed to enrich new disease insights and learnings, delivering value specifically to strategically inform and de-risk clinical drug development activities, and help operationalise clinical trial programs more effectively and efficiently.
What is inClude
inClude learns robust prediction models that characterise undiagnosed and misdiagnosed patient cohorts for difficult to diagnose diseases with a very high accuracy, revealing the natural history of the full target patient population, not just of those already diagnosed.
inClude reveals the heterogeneity in your full target patient population, and allows you to incorporate this in your clinical development strategy, design and execution:
• Manage heterogeneity risks
• Develop differentiating endpoints, in earlier disease states
• Select lead candidate drug based on newly discovered biomarkers
• Align trial operations around newly identified patients
Identifying the right target patient population for
clinical development by characterising patients,
using real world evidence in a new way.
Ready to shape the future of healthcare? Let’s explore how our machine learning solutions can accelerate diagnoses, refine patient journeys, and drive meaningful impact for those who need it most. We value your ideas and expertise, reach out now to unlock new possibilities together.
