inTrigue Disease Learning fundamental to earlier disease diagnosis
Rare diseases take a long time to diagnose. Typically, they are diagnosed five to seven years too late.
This has changed with inTrigue because we learn new biomarkers from electronic health records that allow earlier intervention before coding (ICD9/ICD10/ICPC) has even occurred.
Earlier diagnosis means that prescriptions can be started sooner, leading to better health outcomes for patients and delivering a better return on investment for our clients.
A range of challenges must be overcome in order to achieve earlier diagnosis and a full understanding of the disease. These challenges include the following.
Clinical coding in electronic health records
Symptoms may not be
The disease is often understood differently in
Turning that challenge into an opportunity with inTrigue
Challenging current assumptions to
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Learning diseases & prediction assessment
Automated disease learning is carried out from first principles, producing mathematical representations of diseases as a starting point in the inTrigue methodology. This is done through modelling the semantics of our target rare disease and also its symptoms and comorbidities via representation learning with large medical corpora.
Disease Modelling for Prediction
inTrigue is a standardised repeatable methodology that produces robust models despite the challenges of real-world messy, gappy, sparse and unstructured data, which is how electronic health records (EHRs) are presented.
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, which will deliver empirical and validated prevalence results.
Novel Biomarker Discovery
inTrigue learns new things about the disease like symptoms, comorbidities, natural history and will also define new predictive biomarkers that can be cognitive, digital and physical.
Clinically Interpretable Models
It is desirable to construct prediction models which are both accurate and interpretable, to ensure that clinicians understand the basis for the predictions and recommendations of decision-support systems.
Using new diagnostic tools to detect patients earlier means working in heterogenous environments. To this end, Volv has developed a technical capability and framework that we can use to deploy models in any data setting.