inTrigue

Real world evidence combined with artificial intelligence.
Learning from patients to help other patients to be diagnosed and treated sooner

AI & Data Science

Volv’s approach is unique and provides state of the art, performance-defining cohorts more accurately.

Volv's data science team are specialists at using real world data, both structured and unstructured, to build robust predictive models for better patient outcomes

Btn Connect 80 Whi Grey

Model external medical knowledge

Volv models the semantics of target rare diseases and their symptoms and comorbidities via representation learning with large medical corpora to render a highly dimensional computational model.

FIND OUT MORE >

Btn Data Science 80 Whi Grey

Build robust predictive models with no labels

Volv uses combinations of unsupervised and lightly supervised machine learning methods in a highly dimensional feature space, which would ordinarily require 1,000s of labels to deliver the same level of accuracy. Volv can do this with no labels at all.

FIND OUT MORE >

Btn Valid Results 80 Whi Grey

Validated prevalence

Throughout the inTrigue process we maintain the ability to get external validation of our results and methods. Specialist clinicians are involved in the assessment of the patients being flagged as at risk. This delivers empirical and validated prevalence results.

FIND OUT MORE >

Btn New Biomarkers 80 Whi Grey

Novel Biomarker Discovery

inTrigue learns new things about the disease including new symptoms, comorbidities, natural history. It creates new predictive biomarkers that can be cognitive, digital and physical.

FIND OUT MORE >

Btn Interp Model 80 Whi Grey

Clinically Interpretable Models

Volv's machine learning algorithms, with thousands of features, are impossible for humans to understand. It is desirable to construct different prediction models which are both accurate and human interpretable, so that clinicians can understand the basis for the recommendations of decision-support systems.

FIND OUT MORE >

Btn Federate 80 Whi Grey

Transfer learn and deploy models in diverse healthcare systems

Volv has developed transfer learning techniques, which adapts the models in different data sets so that they can be deployed and maintained in multiple, heterogenous settings.

FIND OUT MORE >

Learn more about Volv Data Science
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value