
Executive Summary For decades, the pharmaceutical industry has faced the same recurring problems with clinical development: the struggle to fully...
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
Detecting, clustering and differentiating sub-groups within a patient cohort is essential for learning more about diseases and thereby improving patient outcomes and optimising treatment plans. Doing so, we ensure a patient receives appropriate and timely care and treatment.
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
Executive Summary For decades, the pharmaceutical industry has faced the same recurring problems with clinical development: the struggle to fully...
Alpha-1 antitrypsin deficiency (AATD), a rare genetic condition, can cause lung disease in adults with symptoms similar to chronic obstructive...
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