Cardiovascular Risk Assessment Agent
An AI-powered clinical decision support system delivering evidence-based cardiovascular disease risk assessments in seconds instead of hours.
The Problem: Manual Process
So here's the problem. Imagine you're a doctor, and a patient walks in with high blood pressure, elevated cholesterol, and they're a smoker. You need to figure out their heart disease risk, but that's just the start.
Then you've got to dig through hundreds of pages of clinical guidelines to find the right treatment protocol. And you need research papers to back up your recommendations. This entire process? It takes lots of time and patience. And during those two hours, you might miss something critical.
Our Solution with Elasticsearch
An AI agent that synthesizes patient data, clinical guidelines, and research evidence into a single clinical report — a 99% reduction in reporting time.
Key outputs
Risk score & top contributing factors
Clear numeric risk estimate with the main drivers explained for quick interpretation.
Evidence-based guideline recommendations
Concise, actionable treatment and care steps aligned to current clinical guidelines.
Cited research references
Direct citations and links to supporting studies for rapid verification.
System Architecture
The agent autonomously orchestrates risk prediction, guideline search, and research retrieval to generate comprehensive clinical reports.
Key Features
Autonomous Multi-Step Reasoning
Agent independently decides which tools to use based on patient data without hardcoded logic
Hybrid Search
Combines semantic vector search with BM25 keyword matching for 30% better retrieval accuracy
ML Risk Prediction
XGBoost model trained on 50,000+ patient records with SHAP interpretability
Evidence-Based Citations
Every recommendation includes guideline citations and supporting research with PMID references
Technical Implementation
Machine Learning Model
Algorithm
Cost-sensitive XGBoost
Training Data
70,000 patient records
Features
14 clinical + derived variables
Performance
80%+ AUC Score
Elasticsearch Features
Agent Builder
Autonomous orchestration
Vector Search
384-dim embeddings
Hybrid Search
30% better retrieval
ES|QL
4x faster queries
Performance Results
8s
Agent Response Time
End-to-end processing
15+
Sources Consulted
Per assessment
10K+
Documents Indexed
Searchable in <1 second
99%
Time Reduction
vs. manual process
Challenges & Solutions
Challenge: Long Clinical Guidelines
Problem: 500-page documents exceeded token limits
Solution: Intelligent chunking by recommendation section
Outcome: Better preprocessing practices
Challenge: Citation Accuracy
Problem: Early versions hallucinated PMID numbers
Solution: Configured agent to cite only retrieved metadata
Outcome: Eliminated hallucination
Challenge: Speed vs. Comprehensiveness
Problem: Initial 30-second response time too slow
Solution: ES|QL optimization + top-5 limiting
Outcome: 4x speedup (30s → 8s) with no quality loss
Future Enhancements
1
Next 3 Months
- EHR integration (FHIR standard)
- Multi-language support
- Mobile app deployment
- Batch processing
2
6-12 Months
- ECG image analysis
- Patient tracking
- Medication checking
- Real-time monitoring
3
1+ Years
- Genomics integration
- Federated learning
- Regulatory approval
- Clinical trials