Health Scoring ML

We worked with a health-tech startup to build a machine learning model that generates personalized health risk scores from wearable data, lab results, and lifestyle questionnaires — giving users actionable insights rather than raw numbers.

The challenge

Users were overwhelmed by data from wearables, lab tests, and health apps but had no way to understand what it meant holistically. Generic health advice didn't account for individual baselines, and clinicians didn't have time to synthesize all the data during short appointments.

What we built

We built a scoring model that ingests multi-source health data, establishes personalized baselines, and generates composite risk scores across key health dimensions. The model explains its scores in plain language and suggests evidence-based actions prioritized by potential impact.

Results

• Users who followed recommendations saw measurable health improvements within 90 days
• Clinician review time per patient reduced by 40%
• Health score engagement drove 3x higher retention than raw data views