Case Study - Custom ECG classification model for clinical signal triage
Built a domain-tuned ECG classification pipeline that helps care teams prioritize abnormal traces faster.
- Year
- Service
- Custom AI model development
Overview
A healthcare operations team needed a model that could classify ECG traces for triage support without replacing clinician judgment.
We designed a full pipeline for waveform normalization, feature extraction, model inference, and confidence-aware routing. High-confidence predictions are surfaced quickly while uncertain traces are escalated to clinician review.
The implementation included model versioning and audit-friendly output traces so every recommendation could be reviewed in context.
What we did
- Signal preprocessing
- Custom classification model
- Clinical review workflow
- Model observability
Cognitive Core AI translated a complex clinical requirement into a model workflow our medical teams could trust and use daily.
- Operational status
- Triage-ready
- Critical decision path
- Human-reviewed
- Model lifecycle
- Versioned
- Prediction traceability
- Auditable