📍 Medellín, Colombia | 💼 Open to Health Data Science & Clinical Analytics Roles
Private practice physician with 13+ years of clinical experience — now transitioning into Health Data Science through the IBM Professional Certificate.
My practice had two core dimensions. In general private medicine, I ran my own clinic managing a broad patient population with direct, longitudinal relationships. In oncology support and palliative care, I served as a clinical bridge: helping patients and families understand complex diagnoses, navigate treatment decisions, and face end-of-life with clarity — often as a last resort when the system had nothing more to offer.
That means I spent 13 years making high-stakes decisions under uncertainty, with incomplete data, where the cost of a wrong call was irreversible. That experience is directly relevant to building clinical decision support systems. I understand what a false negative costs not as a metric, but as a human outcome. I know why model explainability is non-negotiable in healthcare. And I understand the gap between what an algorithm outputs and what a clinician actually needs to act on it.
Foundation: 3 modules of Python for Everybody (University of Michigan) · IBM Data Science Professional Certificate (in progress)
Early Warning Score (EWS) system for ischemic event detection | Full CRISP-DM pipeline
A production-grade cardiovascular risk stratification system built to clinical standards — not a textbook exercise. Designed for real-world deployment with HL7/FHIR integration, HIPAA compliance, and clinician-facing explainability.
| Phase | Notebook | Status | Key Deliverable |
|---|---|---|---|
| 📋 Business Understanding | Fase 1 ↗ | ✅ Complete | EWS strategy · stakeholder analysis · ROI $8.2M |
| 🔍 Data Understanding | Fase 2 ↗ | ✅ Complete | EDA · Spearman heatmap (D3.js) · feature ranking |
| 🛠️ Data Preparation | Fase 3 ↗ | ✅ Complete | Feature engineering (slope_x_oldpeak, log_oldpeak, vessels_bin) · 18 features · SMOTE |
| 🤖 Modeling | Fase 4 ↗ | ✅ Complete | GradientBoosting ganador · Recall=1.000 · AUC=0.997 · GridSearchCV 5-fold |
| 📊 Evaluation | Fase 5 ↗ | ✅ Complete | Test set sellado · Recall=1.000 · AUC=0.9996 · FN=0 · SHAP top predictor: slope |
| 🚀 Deployment | Fase 6 ↗ | ✅ Complete | EWS deployable · 3 niveles (Bajo/Medio/Alto) · umbrales p<0.30/0.65 · artefactos joblib |
Dataset: Cardiovascular Disease — Kaggle · 1,000 clinical records · 18 features (11 original + 3 engineered + 4 OHE)
Key findings:
slope(ST segment pendiente) confirmed as primary predictor — validated by 6 SHAP studies 2023–2026- ECG trio
slope + oldpeak + exerciseangiaprovides strongest class separation - 11.3% of women present ischemic risk with no classic symptoms → gender-specific thresholds required in Fase 3
- Business case: ~$504K/year per institution · $8.2M across 16 institutions at 20% mortality reduction
- Gradient Boosting logró Recall=1.000 en test set sellado (0 falsos negativos)
👉 Master Index — Full Project Navigation
Dataset: 110,000+ Brazilian medical appointments | Status: ✅ Complete
Applied data cleaning, EDA and statistical analysis to identify no-show predictors. 20% no-show rate aligned with my private practice experience in oncology, where missed chemotherapy appointments lead to measurable disease progression.
→ /LABS/course-1-medical-appointments
| Area | Detail |
|---|---|
| General Private Medicine | 13+ years running own clinic, broad patient population, direct longitudinal relationships |
| Oncology Patient Support | Clinical bridge between patients, families and treating physicians — translating complex diagnoses into actionable understanding |
| Palliative & End-of-Life Care | Last-resort support for patients the system had discharged — high-stakes decisions, limited information, irreversible outcomes |
| Clinical Decision-Making | 13 years of diagnostic reasoning under real uncertainty, with real consequences |
| Health Informatics | HL7/FHIR standards, EHR workflows, clinical interoperability |
| Regulatory | HIPAA · Ley 1581 Colombia · patient privacy protocols |
| Certificate | Institution | Issued |
|---|---|---|
| IBM Data Science Professional Certificate (in progress) | IBM / Coursera | — |
| Tools for Data Science | IBM | Feb 2026 |
| What is Data Science? | IBM | Feb 2026 |
| Using Python to Access Web Data | University of Michigan | Jan 2026 |
| Python Data Structures | University of Michigan | Dec 2025 |
| Programming for Everybody (Getting Started with Python) | University of Michigan | Dec 2025 |
IBM Certificate — Course Progress
| Course | Status |
|---|---|
| Course 1: What is Data Science? | ✅ Complete |
| Course 2: Tools for Data Science | ✅ Complete |
| Course 3: Data Science Methodology | ✅ Complete |
| Course 4: Python for Data Science & AI | 🔄 In Progress |
| Course 5–10 | 📋 Upcoming |
Languages: Python · SQL
Libraries: Pandas · NumPy · Matplotlib · Seaborn · SciPy · Scikit-learn (in progress)
Visualization: Chart.js · D3.js · Matplotlib (dark theme) · Seaborn
Tools: Jupyter Notebook · Google Colab · Git / GitHub · VS Code · Anaconda
Domain Standards: HL7/FHIR · HIPAA · CRISP-DM
Next: Scikit-learn ML pipelines · SHAP explainability · MLFlow
ibm-data-science-portfolio/
├── proyecto_medico/
│ └── cardiorisk/ # CardioRisk — Cardiovascular EWS (CRISP-DM completo)
│ ├── Fase1_Business_Understanding.ipynb # ✅ Complete
│ ├── Fase2_Data_Understanding.ipynb # ✅ Complete
│ ├── Fase3_Data_Preparation.ipynb # ✅ Complete | 18 features (3 engineered)
│ ├── Fase4_Modeling.ipynb # ✅ Complete | GradientBoosting Recall=1.0
│ ├── Fase5_Evaluation.ipynb # ✅ Complete | AUC-ROC=0.9996, FN=0
│ ├── Fase6_Deployment.ipynb # ✅ Complete | EWS deployable
│ ├── Master_Index.ipynb # Navigation hub
│ └── README.md # Project documentation
└── LABS/
└── course-1-medical-appointments/ # No-show prediction (110K records) ✅
"After 13 years at the bedside, the highest-leverage decision I could make was to scale clinical insight through data." Life at the service of life