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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

  2244

  PDF: 774
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  Artificial intelligence and machine learning in hemostasis and thrombosis

  Paolo Gresele

  30-12-2023

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

  3582

  PDF: 712
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  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

  1345

  PDF: 249
• 

  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

  Società Italiana di Emostasi e Trombosi

  16-04-2026

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

  87

  PDF: 18