Identifying Diabetic Biomarkers Using Machine Learning Models for a Digital Therapeutics Company

Explore how Reveal used a machine learning (ML) model to identify biomarkers for a digital therapeutics organization.

Identifying the Biomarkers

Our client wanted Reveal’s support to identify biomarkers in patients who are likely to respond to a treatment. Identifying these biomarkers would significantly improve patient outcomes. However, a key challenge was that every patient had unique health characteristics and identifying the relevant biomarkers was uncertain.

Revolution in Digital Therapeutics

Reveal developed an ML model that could predict the A1C (average blood sugar levels) changes at stipulated periods using the client’s digital therapeutic. The model integrated data from several sources such as health trackers, clinical systems, apps used by the patient, and self-reported surveys. It was layered with a provider-facing app to organize the provider’s patient panel for optimal triage usage. Additionally, the solution used observational ML models to inform HEOR and identify target patient populations who are likely to respond to our client’s digital therapeutic.

Impact Achieved

The insights gathered from Reveal’s solution supported a strategic partnership between our client and a key payer partner. They jointly published a paper on biomarker development using machine learning.

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