This paper is in the following e-collection/theme issue:

Pharmacology and Pharmaceutics (70) Machine Learning (1501) Clinical Information and Decision Making (1434) Clinical Informatics (1050)

Published on in Vol 11, No 2 (2022): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34533, first published .
Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Authors of this article:

Ahmad Abdel-Hafez1, 2 Author Orcid Image ;   Ian A Scott3, 4 Author Orcid Image ;   Nazanin Falconer5, 6 Author Orcid Image ;   Stephen Canaris1 Author Orcid Image ;   Oscar Bonilla1 Author Orcid Image ;   Sven Marxen7 Author Orcid Image ;   Aaron Van Garderen1, 7 Author Orcid Image ;   Michael Barras5, 8 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

Journals

  1. Li Q, Tang B, Wu Y, Yao B, Zhang W, Zheng Y, Zhou Y, van den Anker J, Hao G, Zhao W. Machine Learning: A New Approach for Dose Individualization. Clinical Pharmacology & Therapeutics 2024;115(4):727 View
  2. Martucci G, Giani M, Schmidt M, Tanaka K, Tabatabai A, Tuzzolino F, Agerstrand C, Riera J, Ramanan R, Grasselli G, Ait Hssain A, Gannon W, Buabbas S, Gorjup V, Trethowan B, Rizzo M, Fanelli V, Jeon K, De Pascale G, Combes A, Ranieri M, Duburcq T, Foti G, Chico J, Balik M, Broman L, Schellongowski P, Buscher H, Lorusso R, Brodie D, Arcadipane A, Brioni M, Pesenti A, Montini L, Bosa L, Brazzi L, Curcio P, Garofalo E, Martin-Villen L, Garcìa-Álvarez R, Sanchez M, Principe N, Saez V, Freita S, Colomina-Climent J, Pacheco A, Goutay J, Szułdrzyński K, Kowalewski M, Eller P, Lobmeyr E, Mariani S, Suk P, Maly M, Forestier J, Rizzitello N, Barbara M, Holsworth T, Serra A, Cavayas Y, Menaker J, Galvagno S, Rice T, Grandin W, Nunez J, Cheplic C, Subramanian H, Rivosecchi R, Cho Y, Kwan M, Sallam H, Villanueva J, Aliudin J, Hoshino K, Hara Y, Ramanathan K, Maclaren G. Anticoagulation and Bleeding during Veno-Venous Extracorporeal Membrane Oxygenation: Insights from the PROTECMO Study. American Journal of Respiratory and Critical Care Medicine 2024;209(4):417 View
  3. Falconer N, Scott I, Barras M. Powered by AI: advancing towards artificial intelligence algorithms in Australian hospital pharmacy. Journal of Pharmacy Practice and Research 2024;54(2):107 View
  4. Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thrombosis and Haemostasis 2024;124(11):1040 View
  5. Scott I, De Guzman K, Falconer N, Canaris S, Bonilla O, McPhail S, Marxen S, Van Garderen A, Abdel-Hafez A, Barras M. Evaluating automated machine learning platforms for use in healthcare. JAMIA Open 2024;7(2) View
  6. Rylnikov D, Seliverstov E, An E, Kuperin A, Lebedev I, Zolotukhin I. Anticoagulant Therapy Flaws in Hospitalized Patients with Proximal Deep Vein Thrombosis and Their Impact on the Treatment Outcomes. Journal of Venous Disorders 2024;18(3):178 View