Thrombophilia work-up and clinical outcomes in Indian patients with unprovoked venous and arterial thrombosis aged 18-50 years | BTVB Bioequivalence of von Willebrand factor-containing concentrates: implications for von Willebrand disease | BTVB Cardiovascular risk in hormone replacement therapy | BTVB Direct oral anticoagulants: does one dose fit all? Why, when and how testing | BTVB Cardiovascular risk profile evolution over 15 years: CV-PREVITAL vs Moli-sani Study | BTVB Launch of National Registry for Congenital Hematological Disorders in Palestine: Haemo-PAL | BTVB

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Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot

Rushad Patell, Jeffrey I. Zwicker, Rohan Singh, Simon Mantha

16-05-2024

https://doi.org/10.4081/btvb.2024.123

2415

PDF: 975
Artificial intelligence and machine learning in hemostasis and thrombosis

Paolo Gresele

30-12-2023

https://doi.org/10.4081/btvb.2023.105

3869

PDF: 770
PO61 | RISK FACTORS FOR INTRACRANIAL HEMORRHAGE: AN UMBRELLA REVIEW TO INFORM MACHINE LEARNING PREDICTION MODELS IN GLIOMA PATIENTS RECEIVING ANTICOAGULATION T.A. Adeyemo, S. Greenley, F. Ware, W. Jones, A. Maraveyas, F. Haque | ¹Centre of Excellence for Data Science, Artificial Intelligence and Modelling DAIM, Faculty of Science and Engineering, University of Hull, UK; ²Hull York Medical School, Hull, UK; ³Academic Library Services, University of Hull, UK; ⁴NHS Humber Health Partnership, Hull, UK

ICTHIC 2026

16-04-2026

https://doi.org/10.4081/btvb.2026.552

334

PDF: 91
The evolution of hemostasis genetics: from monogenic disorders to complex traits. A historical perspective

Augusto Di Castelnuovo

01-07-2025

https://doi.org/10.4081/btvb.2025.184

1493

PDF: 306

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