Clinical Data Analysis & Annotation Platform (MIMIC Data System)

// tech stack

// about

Overview

This project is a clinical data analysis and annotation platform designed for doctors to analyze patient data from multiple medical sources and provide structured insights for AI training.

The system integrates high-volume clinical data such as ECG signals, vital metrics, and event logs, and presents them in an interactive, time-synchronized interface.

Problem Statement

Doctors require a unified system to:

• ▹View and analyze patient events over time
• ▹Correlate clinical events with vital signals
• ▹Interpret ECG and physiological data accurately
• ▹Provide structured annotations and reasoning for diagnosis
• ▹Generate high-quality labeled data for AI model training

Handling large, multi-source medical datasets while maintaining usability and performance is a major challenge.

Solution

I built a frontend platform that provides a synchronized and interactive view of patient data, enabling doctors to analyze and annotate clinical events efficiently.

Key Features

1. Timeline-Based Event Visualization

• ▹Implemented an interactive timeline showing all clinical events (admission, procedures, prescriptions, chart events)
• ▹Displayed event counts and categories for quick overview
• ▹Enabled precise navigation across different time blocks

2. Multi-Source Data Integration

• ▹Integrated multiple clinical data streams including:
  • ▹ECG signals
  • ▹Vital signs (HR, SPO2, RR)
  • ▹Medical event logs
• ▹Ensured all data is time-synchronized for accurate analysis

3. Advanced Graph & ECG Visualization

• ▹Built dynamic charts for vitals and ECG signals
• ▹Enabled zooming, time-range selection, and detailed inspection
• ▹Displayed macro-level trends along with micro-level ECG segments

4. Contextual Data Exploration

• ▹Allowed doctors to view detailed notes and event metadata
• ▹Provided a structured view of procedures, prescriptions, and outputs
• ▹Enabled correlation between events and physiological changes

5. Annotation & AI Data Pipeline

• ▹Enabled doctors to assign criticality scores and provide reasoning
• ▹Structured outputs designed to feed AI models for training
• ▹Ensured consistency and accuracy in data labeling

Impact

• ▹Simplified complex clinical data analysis into a single unified interface
• ▹Improved doctor efficiency in reviewing patient history and events
• ▹Enabled creation of high-quality labeled datasets for AI training
• ▹Bridged the gap between raw medical data and AI-ready structured insights

Key Learnings

• ▹Handling multi-source medical data requires precise synchronization
• ▹UI clarity is critical when dealing with complex, high-density information
• ▹Performance optimization is essential for large datasets and interactive graphs
• ▹Designing for domain users (doctors) requires deep focus on usability and accuracy