inTrigue

Real world evidence 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.

We are specialists at managing real world data, both structured and unstructured, to build robust predictive models for better patient outcomes

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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.

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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 typically require 50,000 labels to produce a traditional model with equivalent accuracy. Often, we have no labels at all to work with.

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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 being at risk, and this delivers empirical and validated prevalence results.

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Novel Biomarker Discovery

inTrigue learns new things about the disease, like new symptoms and comorbidities. inTrigue will also define new predictive biomarkers that can be cognitive, digital or physical.

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Clinically Interpretable Models

It is desirable to construct prediction models which are both accurate and human interpretable. It is essential that clinicians understand the basis for the predictions and recommendations of decision-support systems.

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Transfer learn & deploy models in diverse healthcare systems

Volv uses unique transfer learning techniques to adapt its models to different data sets, so that they can be deployed and maintained in multiple heterogenous settings.

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