I'm a physician-researcher focused on cardiovascular medicine, cardiac imaging, and clinical AI. My work centers on developing and evaluating machine-learning models for diagnosis and prognosis — particularly in echocardiography and ultrasound-based cardiovascular assessment — models that are accurate, well-calibrated, validated, and genuinely useful at the bedside.
🏥 Research Assistant at Shaheed Rajaie Cardiovascular Medical & Research Center, Tehran, Iran
🎓 MD, Shahid Beheshti University of Medical Sciences (2017–2024)
🔬 Research focus: cardiac imaging AI (echocardiography, ultrasound, cardiac CT, CMR), risk prediction for heart failure/stroke/valvular disease, ML model evaluation & external validation, evidence synthesis & meta-analysis of AI-based diagnostic/prognostic models
🧰 Core skills: systematic review & meta-analysis, diagnostic test accuracy synthesis, clinical prediction modeling, discrimination/calibration assessment, statistical analysis in R, reproducible research workflows
📈 250+ citations · h-index 10 · 60+ publications
🌐 More at pooyaeini.github.io
| Project | Description | Stack | Stars |
|---|---|---|---|
| catheterization-ML | Catheterization prediction using ML models — clinical decision support for invasive cardiac procedures | Python, scikit-learn, XGBoost, SHAP | ⭐⭐ |
| Right-Ventricular-Dysfunction-in-Acute-Pulmonary-Embolism | ML pipeline predicting RV dysfunction in acute pulmonary embolism from clinical & imaging data | Python, PyTorch, scikit-learn, echocardiography | ⭐⭐ |
| vitaldb-arrhythmia-hrv-ml | HRV/ML pipeline predicting intraoperative arrhythmia onset from VitalDB arrhythmia database annotations | Python, HRV analysis, PyTorch, scikit-learn | ⭐⭐⭐ |
| lab-data-extractor | AI-based laboratory data extraction from clinical documents | Python, NLP, OCR, LLMs | ⭐⭐⭐ |
| zzu-pecg-cvd-ml | Interpretable ML detection of cardiovascular disease from structured pediatric ECG reports (ZZU-pECG) | Python, scikit-learn, SHAP, interpretability | ⭐⭐ |
