Maha Othman: How Artificial Intelligence and Machine Learning Can Improve Risk Assessment of Cancer-Associated Thrombosis
Maha Othman, Co-Chairman SSC on Disseminated intravascular coagulopathy at International Society on Thrombosis and Haemostasis, shared Thrombosis and Hemostasis’s a post on LinkedIn:
“Proud of these three brilliant young scientists Adham E. Ali Zidan and Stefania Coroneos for their outstanding work exploring current literature on how artificial intelligence and machine learning can improve risk assessment of cancer-associated thrombosis.
This Seminars in Thrombosis and Hemostasis publication has laid foundation to explore best suited tools, and enabled us to leverage advanced computational approaches to identify patterns that may enhance our ability to predict thrombotic risk, support personalized care, and ultimately improve outcomes for women with cancer.
We are still in the discovery phase and more work is ahead of us!
On this note, I take the opportunity to congratulate George Ilbawi on taking this further and on the recent acceptance of his abstract at IGCS 2026 ‘Enhancing The Feasibility Of Machine Learning For Predicting Cancer-Associated Thrombosis In Small Clinical Cohort Study’.
Thanks to the special support of our data analyst collaborator Sima Afshar Nezhad and of course the contribution of members of my group Yousra Tera Regan Bucciol Adham E. not forgetting the foundational support of our clinican collaborators Queen’s University departments of oncology and gynaecology.”
Seminars in Thrombosis and Hemostasis shared a post on LinkedIn about a recent article by Adham H. El-Sherbini and his colleagues, adding:
“Most Popular Article Award – Free Access
- Machine Learning for Predicting Thrombosis in Cancer Patients
A powerful review highlighting how AI-driven models are transforming hashtag#thrombosis risk prediction in oncology.”
Title: Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review
Authors: Adham H. El-Sherbini, Stefania Coroneos, Ali Zidan, Maha Othman
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