Unsupervised Machine Learning for Patient Stratification in Cardiovascular Disease
Ahmed Bennis, Professor of Cardiology at Ibn Rochd University Hospital, shared on X:
” Unsupervised machine learning for cardiovascular disease. A framework for future studies.
Unsupervised machine learning can improve the characterization and stratification of patients with cardiovascular diseases (CVDs).
Clustering algorithms, which group patients based on patterns in clinical data, can reveal distinct subgroups that may differ in prognosis and treatment response.
Read the full article here.
Article: Unsupervised machine learning for cardiovascular disease: A framework for future studies
Authors:Emmanuel Bresso, Claire Lacomblez, Kévin Duarte, Luca Monzo, Guillaume Baudry, Jasper Tromp, Abhinav Sharma, Nicolas Girerd

Stay updated with Hemostasis Today.
-
Feb 23, 2026, 18:13Fight4Hematology Supports Research and Empowers the Next Generation – ASH
-
Feb 23, 2026, 17:59Wolfgang Miesbach: Real-World Evidence of Emicizumab on Joint Outcomes in Hemophilia A
-
Feb 23, 2026, 17:56Shiny K Kajal: The Transfusion Reaction We Often Miss
-
Feb 23, 2026, 17:53Radheshyam Meher: Contributing to the Transfusion Evidence Round-Up for International Childhood Cancer Day 2026
-
Feb 23, 2026, 17:46Mahesan Subramaniam: The Physiological Impact of Anger on Immunity
-
Feb 23, 2026, 17:42Bryan Fry: First Evidence That Bothrops atrox Venom Directly Activates Human Factor VII
-
Feb 23, 2026, 17:34Bastu Odoka: Why Blood Should NOT be Left at the Bedside to ‘Warm’
-
Feb 23, 2026, 17:28Henry Burkitt: Patients Are Challenging How the Medicines Policy System Works in England
-
Feb 23, 2026, 16:50Mutaz Al‑Sabah: Interesting Webinar on FH in Women is Now Available to Watch