13/05/2024/
Alpha-1 antitrypsin deficiency (AATD), a rare genetic condition, can cause lung disease in adults with symptoms similar to chronic obstructive...
Volv Global’s inTrigue machine learning technology is able to map out heterogeneous populations and cluster patients into sub-groups according to learned biomarkers. One example is in differentiating and detecting cases of fast progressors
This ensures patients receive personalised treatment appropriate to their situation. In the example, a sub-cohort of patients may progress to a more severe form of a disease more quickly than others and therefore require different treatment.
Why speed matters
By identifying, clustering, and differentiating patient sub-groups, we can better understand diseases, improve patient outcomes, and optimise treatment plans—ensuring patients receive timely and appropriate care.
Patients with fast disease progression need a rapid response to ensure interventions that best fit their needs. Timely flagging of fast progressors enables best case treatment for every patient.
Every patient is different. Detecting specific clusters and tailoring therapies to their specific needs leads to individualised treatment for each patient and an optimal patient journey.
Clinical trials benefit from a precisely defined patient cohort. Identifying the right candidates more accurately bolsters clinical trial recruitment and leads to more effective trials.
Volv Global’s inTrigue powers solutions across stakeholders
More accurate definition of trial inclusion and exclusion criteria.
More precise stratification of patient cohorts for value and outcomes research.
Better understanding of patient phenotypes for better diagnosis.
More relevant targeting of commercial outreach programmes.
Industry-leading technology at work
inTrigue uses advanced AI and machine learning algorithms to analyse patient data, identifying patterns and indicators which can serve to differentiate sub-groupings of patients. Our technology considers various factors, including genetic information, medical history, and real-world evidence, to provide a comprehensive assessment.
Transformative real-world impact
Our ability to differentiate patient clusters, e.g., fast progressors, has real-world implications for patient care.
Here are a few examples of how Volv Global and inTrigue have made a difference:
Shaping the future today
Alpha-1 antitrypsin deficiency (AATD), a rare genetic condition, can cause lung disease in adults with symptoms similar to chronic obstructive...
By Volv Global SA and WODC EU contributors Executive Summary For decades, the pharmaceutical industry has faced the same...
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.
