🏥 TrailNav: AI Clinical Matcher & Cardological Research Tool

TrailNav is an advanced medical platform that combines Generative AI for clinical trial matching with Explainable ML for cardiovascular risk assessment. It aims to streamline the workflow for medical researchers and clinicians by bridging the gap between clinical notes and actionable data.

Automates the process of matching patients to clinical trials using Google Gemini 2.0 Flash.

• Intelligent Extraction: Parses unstructured notes into structured JSON patient profiles.
• Hybrid Matching: Combines Gemini's reasoning with a fast, local keyword-based fallback system.
• FHIR Integration: Built with interoperability in mind using FHIR-ready data schemas.

An explainable machine learning module for predicting Cardiovascular Disease risk.

• Predictive Modeling: Uses a Random Forest classifier trained on UCI Heart Disease datasets.
• Explainability (SHAP): Provides personalized risk assessments with visual evidence of contributing factors.
• Research Ready: Includes EDA pipelines and interpretability reports.

• Modern Dashboard: Responsive UI built with Framer Motion for smooth, premium transitions.
• Speed & Reliability: Optimized pipeline ensures fast matches even when LLM APIs are rate-limited.
• Privacy Focused: Direct API integration with Google AI Studio—no intermediate data storage.
• Developer Friendly: Fully documented FastAPI backend with interactive Swagger & ReDoc.

TrailNav uses a multi-stage pipeline:

1. Extractor: Extracts markers (Diagnosis, ECOG, Biomarkers) from notes.
2. Retriever: Fetches real-time trials via ClinicalTrials.gov API.
3. Reasoner: Multi-step AI validation for eligibility criteria.
4. CVD Engine: Parallel ML processing for cardiac risk profiling.

• Python 3.10+
• Node.js 18+
• Google Gemini API Key

# Clone the repository
git clone https://github.com/vrushabhzade/Trailnav.git
cd Trailnav

# Backend Setup
# It is recommended to use a virtual environment
pip install -r requirements.txt
cp .env.example .env  # Add your GEMINI_API_KEY to .env

# Start Backend
python api/main.py

# Frontend Setup
npm install
npm run dev

Once the backend is live at http://localhost:8001:

• Swagger UI: /docs
• ReDoc: /redoc

Contributions are welcome! If you find a bug or have a feature request, please open an issue or submit a pull request.

This project is licensed under the MIT License.