Data Systems / ML Systems · Healthcare · internal
ECG Intelligence Platform
Vendor-agnostic clinical data unification for ML readiness.
Unified heterogeneous PDF, image, CSV, and waveform inputs into a canonical clinical schema with configurable field mapping—eliminating recurring manual analyst work and enabling analytics/ML pipelines.
Clinical signal fusion into one trusted dataset
My role
Data / ML Infrastructure Engineering
- Data Pipelines
- AI Architecture
- Backend Engineering
- Productionization
Period / 2024–2025
Overview
Unified heterogeneous PDF, image, CSV, and waveform inputs into a canonical clinical schema with configurable field mapping—eliminating recurring manual analyst work and enabling analytics/ML pipelines.
Problem
Clinical ECG-related data arrives in incompatible vendor formats. Analysts spend hours normalizing fields before analytics or ML training can begin.
System
A configurable normalization and field-mapping pipeline that produces a vendor-agnostic canonical schema suitable for downstream analytics and machine-learning readiness.
System anatomy
Architecture
Heterogeneous intake → normalize → canonical → ML readiness.
- Multi-format intake
- Field mapping
- Canonical schema
- Downstream pipelines
Architecture
PDF, image, CSV, and waveform sources flow through extraction and normalization into a vendor-agnostic canonical schema consumed by analytics and ML pipelines.
- PDF → Extraction
- Image → Extraction
- CSV → Extraction
- Waveform → Extraction
- Extraction → Normalization
- Normalization → Canonical Schema
- Canonical Schema → Analytics / ML Pipeline
Engineering decisions
Canonical clinical schema
Vendor formats diverge. A single canonical model decouples analytics/ML from upstream format churn.
Configurable field mapping
New vendors and fields appear continuously. Mapping configuration avoids hard-coded parsers for every source.
ML-ready normalization
The pipeline targets reusable training/analytics inputs, not one-off analyst spreadsheets.
Reliability / production
- Configurable field mapping
- Heterogeneous input handling
- Reusable normalization workflows
Stack
Outcomes
- Heterogeneous clinical inputs mapped into a canonical schema.
- Reusable normalization pipelines for analytics and ML training.
- Approximately 3–4 hours of recurring manual analyst effort eliminated per week.
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