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

doi:10.1515/cclm-2021-0216

Editorial

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

doi: 10.1515/cclm-2021-0216.

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.

PMID: 33660488
DOI: 10.1515/cclm-2021-0216

Free article

Editorial

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

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.

Authors

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.

PMID: 33660488
DOI: 10.1515/cclm-2021-0216

No abstract available

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

PubMed Disclaimer

Cited by

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning.
Cui R, Hua W, Qu K, Yang H, Tong Y, Li Q, Wang H, Ma Y, Liu S, Lin T, Zhang J, Sun J, Liu C. Cui R, et al. Front Med (Lausanne). 2021 Dec 3;8:775047. doi: 10.3389/fmed.2021.775047. eCollection 2021. Front Med (Lausanne). 2021. PMID: 34926518 Free PMC article.

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.

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions

LinkOut - more resources

Full Text Sources
- De Gruyter
Other Literature Sources
- scite Smart Citations
Medical
- MedlinePlus Health Information

[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].

[2] 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].

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Similar articles

Cited by

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical