Alessandro Oliva
While studying Medicine, I developed a strong interest in Machine Learning, as I became fascinated by the profound revolution taking place in the field during those years. After graduating, I worked as a Research Physician in Cardiology at San Raffaele Hospital (Milan) where I explored applications of ML in that field, focusing on prediction algorithms of cardiovascular recovery after acute care. I now work as a Clinical AI Scientist at Kalibios AG.
Machine Learning Specialization
2025 - 2026Stanford University Online
Focused study on ML algorithms and practical implementation, taught by Prof. Andrew Ng.
Master's Degree in Medicine and Surgery
2018 - 2024San Raffaele University, Milan
Thesis in Cardiac Electrophysiology: “Management of persistent left atrial appendage thrombosis in patients with atrial fibrillation on anticoagulation therapy”.
Clinical AI Scientist
2025 - presentKalibios AG, Basel
Developing the clinical AI logic and backend services for a digital health platform focused on longevity coaching and chronic disease prevention.
Junior Associate Editor
2025 - presentInternational Journal of Cardiology: Innovations | Elsevier
Responsible for AI/ML-focused cardiology submissions (editorial triage, reviewer selection, editorial recommendations).
Research Physician
2024 - 2025Cardiology Department, San Raffaele Hospital, Milan
Research on ML applications in cardiology.
A full-stack AI assistant for clinicians that answers clinical questions with citations to guideline sources.
- Designed a multi-modal guideline processing pipeline handling text, tables, and figures with Pydantic-validated schema extraction and automated entity linking.
- Built a Python RAG pipeline to parse clinical guidelines (ESC, AHA), extract structured content (image → JSON via vision models), and construct a knowledge graph of clinical entities, thresholds, drug classes, and decision pathways.
- Implemented hybrid retrieval combining semantic search (OpenAI embeddings + MongoDB Atlas Vector Search) with graph queries, enabling contextual retrieval and traceable guideline citations.
The soaring potential of machine learning in CAD prediction
2025A. Oliva, F. Robotti
Machine Learning Forecast of Discharge 6MWT after Cardiac Rehabilitation: an Elastic Net Approach Using Acute Phase Data from the EU-Next Generation PROMETEO Registry
2025A. Oliva, F. Robotti, A. Fuga, F. Di Salvo, G. Novembre, S. Gonnella, C. Riccio, A. Cesaro, M. Loguercio, M. Ambrosetti, P. Calabrò, N. Morici, D. Cianflone
Research abstract presented at Change In Cardiology 2025, Turin, Italy
The "No-cut technique" for lead preparation in transvenous lead extraction
2025M. Baroni, A. Preda, L.F. Milillo, A. Oliva, L. Rampa, R. Falco, M. Carbonaro, S. Vargiu, M. Varrenti, G. Colombo, F. Guarracini, L. Gigli, A. Frontera, G. Paglino, A. Marzi, P. Della Bella, P. Mazzone
A Novel Score to Predict Left Atrial Appendage Thrombus and Sludge in Patients with Persistent Atrial Fibrillation on Chronic Anticoagulants
2024L. Rampa, C. Gaspardone, D. Cianflone, G. Scalisi, A. Oliva, A. Preda, G. Fiore, E. Agricola, P. Della Bella