Machine learning and coagulation testing: the next big thing in hemostasis investigations?

Emmanuel J Favaloro^{1

2}, Davide Negrini³

Affiliations

¹ Department of Haematology, Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, NSW, Australia.
² School of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia.
³ Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy.

Free article

Editorial

Emmanuel J Favaloro et al. Clin Chem Lab Med. 2021.

Free article

. 2021 Mar 3;59(7):1177-1179.

doi: 10.1515/cclm-2021-0216.

Emmanuel J Favaloro^{1

2}, Davide Negrini³

¹ Department of Haematology, Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, NSW, Australia.
² School of Biomedical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia.
³ Section of Clinical Biochemistry, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy.

No abstract available

Keywords: blood coagulation tests; clot detection; machine learning.

References

1. Favaloro, EJ, Mohammed, S, Vong, R, McVicker, W, Chapman, K, Swanepoel, P, et al.. Verification of the ACL Top 50 family (350, 550 and 750) for harmonisation of routine coagulation assays in a large network of 60 laboratories. Am J Clin Pathol 2021. https://doi.org/10.1093/AJCP/AQAB004 [Epub ahead of print].
1. Lippi, G, Favaloro, EJ. Preanalytical issues in hemostasis and thrombosis testing. Methods Mol Biol 2017;1646:29–42. https://doi.org/10.1007/978-1-4939-7196-1_2.
1. Mohammed, S, Ule Priebbenow, V, Pasalic, L, Favaloro, EJ. Development and implementation of an expert rule set for automated reflex testing and validation of routine coagulation tests in a large pathology network. Int J Lab Hematol 2019;41:642–9. https://doi.org/10.1111/ijlh.13078.
1. Lippi, G, Plebani, M, Favaloro, EJ. Interference in coagulation testing: focus on spurious hemolysis, icterus, and lipemia. Semin Thromb Hemost 2013;39:258–66. https://doi.org/10.1055/s-0032-1328972.
1. Fang, K, Zheqing Dong, Z, Chen, X. Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med 2021;59;1289–97.