This is a demo and should not be used for real world decision making.
An AI-powered clinical decision support tool for evidence-based PCI vs. CABG recommendations, grounded in the 2021 ACC/AHA/SCAI Coronary Revascularization Guidelines.
Project page: decisionsupport.sharduldhande.com
App: app.decisionsupport.sharduldhande.com (password-protected only to prevent Gemini API abuse)
| Field | Value |
|---|---|
| Username | admin |
| Password | gatech |
Upload any ICA or CCTA report (PDF) to see a guideline-grounded recommendation in seconds.
Choosing between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for a patient with multi-vessel coronary artery disease is one of the most consequential decisions in cardiology. The optimal choice depends on dozens of interacting variables — coronary anatomy, lesion complexity, comorbidities, LV function — and must align with class-of-evidence recommendations from clinical guidelines.
This tool automates that reasoning. A cardiologist uploads a cath lab or CCTA report, and the system:
- Extracts structured clinical findings from the PDF using a large language model
- Retrieves the most relevant ACC/AHA guideline sections via semantic search
- Generates a recommendation (PCI, CABG, or medical management) with guideline citations, confidence level, and a breakdown of factors favoring each approach
Recommendations are grounded in the actual guideline text — not general medical knowledge — which reduces the risk of hallucination and ensures every recommendation is traceable to a specific Class/Level-of-Evidence statement.
Input PDF ICA/CCTA Report
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Extract structured JSON (anatomy, stenosis, comorbidities)
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PubMedBERT + ChromaDB → Retrieve relevant guideline chunks
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Prepare Recommendation based on Extract and JSON (strategy, confidence, PCI vs CABG)
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Formatted Output with Citations
For each case, three distinct queries are issued to ChromaDB and the results are merged:
- A summary of the clinical findings
- A disease-specific query (e.g., "NSTEMI revascularization 2-vessel disease")
- A generic decision query (e.g., "PCI versus CABG multivessel coronary artery disease")
This ensures both patient-specific and general guideline sections are surfaced.
| Component | Technology |
|---|---|
| UI | Streamlit |
| Extraction LLM | Google Gemini 2.5 Flash |
| Recommendation LLM | Google Gemini 2.5 Flash |
| Embeddings | PubMedBERT (NeuML/pubmedbert-base-embeddings) |
| Vector Database | ChromaDB |
| Guideline Source | 2021 ACC/AHA/SCAI Coronary Revascularization Guidelines |
| Runtime | Python 3.13 |
- RAG (Retrieval-Augmented Generation): Guideline sections are retrieved at runtime via vector similarity search, not hard-coded rules. The system can surface any relevant section across the full guideline document.
- Domain-specific embeddings: PubMedBERT, trained on biomedical literature, produces significantly more accurate embeddings for clinical text than general-purpose models.
- Structured extraction with schema validation: The extraction prompt defines a 60+ field JSON schema with explicit clinical rules (e.g., how to interpret qualitative stenosis descriptors like "mild" or "severe", how to determine vessel dominance, what counts as a "significant" lesion).
- Citation transparency: Every recommendation includes the specific guideline section title, Class of Recommendation, Level of Evidence, and the verbatim guideline text that supports it.
- Python 3.10+
- A Google AI Studio API key (free tier works)
# Clone the repository
git clone https://github.com/sharduldhande/decision-support.git
cd decision-support
# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txtexport GEMINI_API_KEY=your_api_key_hereIngest the ACC/AHA guidelines into the vector database (run once):
python ingest.pyThis parses the guideline EPUB, splits it into sections, embeds each section with PubMedBERT, and stores them in ChromaDB.
streamlit run app.py├── app.py # Streamlit app + full AI pipeline
├── ingest.py # One-time guideline ingestion script
├── requirements.txt # Python dependencies
├── ACC_AHA_Guidelines.epub # Source guideline document
└── chroma_db/ # Persistent vector database (created by ingest.py)
NSTEMI (Non-ST-Elevation Myocardial Infarction) is a type of heart attack where one or more coronary arteries are partially blocked. Many NSTEMI patients have multi-vessel coronary artery disease (CAD), requiring a decision about revascularization strategy:
- PCI (Percutaneous Coronary Intervention): A catheter-based procedure that opens blocked arteries using balloons and stents. Less invasive, shorter recovery.
- CABG (Coronary Artery Bypass Grafting): Open-heart surgery that reroutes blood flow around blocked vessels using grafts. Generally preferred for complex multi-vessel disease or left main stenosis.
The 2021 ACC/AHA/SCAI guidelines provide class-of-recommendation and level-of-evidence ratings for specific anatomical and clinical scenarios. Applying these guidelines correctly requires integrating many variables simultaneously — a task well-suited to AI-assisted decision support.