Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter March 3, 2021

Using machine learning to identify clotted specimens in coagulation testing

Kui Fang, Zheqing Dong, Xiling Chen, Ji Zhu, Bing Zhang, Jinbiao You, Yingjun Xiao and Wenjin Xia

Abstract

Objectives

A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens.

Methods

The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset.

Results

Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958–0.974) and 0.971 (95% CI, 0.9641–0.9784), respectively.

Conclusions

Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.


Corresponding author: Zheqing Dong, Director, Clinical Laboratory, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou310000, P.R. China, E-mail:

Funding source: Department of Health of Zhejiang Province

Award Identifier / Grant number: 2021KY845

  1. Research funding: This work was supported by the Science Fund of the Health Department of Zhejiang Province. Project ID: 2021KY845.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Ethical approval: The study was approved by the institutional research ethics committee of The Third Affiliated Hospital of Zhejiang Chinese Medical University. Approval ID: ZSLL-KY-2021-001-01.

References

1. Adcock Funk, D, Lippi, G, Favaloro, E. Quality standards for sample processing, transportation, and storage in hemostasis testing. Semin Thromb Hemost 2012;38:576–85. https://doi.org/10.1055/s-0032-1319768.Search in Google Scholar

2. Das, N, Topalovic, M, Janssens, W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med 2018;24:117–23. https://doi.org/10.1097/mcp.0000000000000459.Search in Google Scholar

3. Deo, RC. Machine learning in medicine. Circulation 2015;132:1920–30. https://doi.org/10.1161/circulationaha.115.001593.Search in Google Scholar

4. Cleophas, TJ, Cleophas, TF. Artificial intelligence for diagnostic purposes: principles, procedures and limitations. Clin Chem Lab Med 2010;48:159–65. https://doi.org/10.1515/cclm.2010.045.Search in Google Scholar

5. Givens, TB, Braun, P, Fischer, TJ. Predicting the presence of plasma heparin using neural networks to analyze coagulation screening assay optical profiles. Comput Biol Med 1996;26:463–76. https://doi.org/10.1016/s0010-4825(96)00023-6.Search in Google Scholar

6. Han, Q, Zheng, W, Guo, X-D, Zhang, D, Liu, H-F, Yu, L, et al.. A new predicting model of preeclampsia based on peripheral blood test value. Eur Rev Med Pharmacol Sci 2020;24:7222–9. https://doi.org/10.26355/eurrev_202007_21874.Search in Google Scholar PubMed

7. Winter, WE, Flax, SD, Harris, NS. Coagulation testing in the core laboratory. Lab Med 2017;48:295–313. https://doi.org/10.1093/labmed/lmx050.Search in Google Scholar PubMed

8. Mishra, A, Ashraf, MZ. Using artificial intelligence to manage thrombosis research, diagnosis, and clinical management. Semin Thromb Hemost 2020.10.1055/s-0039-1697949Search in Google Scholar PubMed

9. Gunnur Dikmen, Z, Pinar, A, Akbiyik, F. Specimen rejection in laboratory medicine: necessary for patient safety? Biochem Med 2015;25:377–85. https://doi.org/10.11613/bm.2015.037.Search in Google Scholar

10. Van Der Maaten, L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 2015;15:3221–45.Search in Google Scholar

11. Bergmeir, C, Benítez, JM. Neural networks in R using the stuttgart neural network simulator: RSNNS. J Stat Softw 2012;46. https://doi.org/10.18637/jss.v046.i07.Search in Google Scholar

12. Cabitza, F, Banfi, G. Machine learning in laboratory medicine: waiting for the flood? Clin Chem Lab Med 2018;56:516–24. https://doi.org/10.1515/cclm-2017-0287.Search in Google Scholar PubMed

13. Smith, SA, Travers, RJ, Morrissey, JH. How it all starts: initiation of the clotting cascade. Crit Rev Biochem Mol Biol 2015;50:326–36. https://doi.org/10.3109/10409238.2015.1050550.Search in Google Scholar PubMed PubMed Central

14. Chee, Y. Coagulation. J R Coll Physicians Edinb 2014;44:42–5. https://doi.org/10.4997/jrcpe.2014.110.Search in Google Scholar

15. Magnette, A, Chatelain, M, Chatelain, B, Ten Cate, H, Mullier, F. Pre-analytical issues in the haemostasis laboratory: guidance for the clinical laboratories. Thromb J 2016;14:49. https://doi.org/10.1186/s12959-016-0123-z.Search in Google Scholar PubMed PubMed Central

16. Dahlbäck, B. Blood coagulation and its regulation by anticoagulant pathways: genetic pathogenesis of bleeding and thrombotic diseases. J Intern Med 2005;257:209–23. https://doi.org/10.1111/j.1365-2796.2004.01444.x.Search in Google Scholar PubMed

17. Zeerleder, S. C1-inhibitor: more than a serine protease inhibitor. Semin Thromb Hemost 2011;37:362–74. https://doi.org/10.1055/s-0031-1276585.Search in Google Scholar PubMed

18. Thachil, J, Lippi, G, Favaloro, EJ. D-dimer testing: laboratory aspects and current issues. In: Favaloro, EJ, Lippi, G, editors. New York, NY: Springer; 2017;91–104, vol 1646.10.1007/978-1-4939-7196-1_7Search in Google Scholar PubMed

19. Wells, PS. Integrated strategies for the diagnosis of venous thromboembolism. J Thromb Haemost 2007;5:41–50. https://doi.org/10.1111/j.1538-7836.2007.02493.x.Search in Google Scholar PubMed

20. Buchtele, N, Schober, A, Schoergenhofer, C, Spiel, AO, Mauracher, L, Weiser, C, et al.. Added value of the DIC score and of D-dimer to predict outcome after successfully resuscitated out-of-hospital cardiac arrest. Eur J Intern Med 2018;57:44–8. https://doi.org/10.1016/j.ejim.2018.06.016.Search in Google Scholar PubMed

21. Weitz, JI, Fredenburgh, JC, Eikelboom, JW. A test in context: D-dimer. J Am Coll Cardiol 2017;70:2411–20. https://doi.org/10.1016/j.jacc.2017.09.024.Search in Google Scholar PubMed

22. Naugler, C, Church, DL. Automation and artificial intelligence in the clinical laboratory. Crit Rev Clin Lab Sci 2019;56:98–110. https://doi.org/10.1080/10408363.2018.1561640.Search in Google Scholar PubMed

23. LeCun, Y, Bengio, Y, Hinton, G. Deep learning. Nature 2015;521:436–44. https://doi.org/10.1038/nature14539.Search in Google Scholar PubMed

24. Gruson, D, Helleputte, T, Rousseau, P, Gruson, D. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 2019;69:1–7. https://doi.org/10.1016/j.clinbiochem.2019.04.013.Search in Google Scholar PubMed

25. Lyu, J, Zhang, J. BP neural network prediction model for suicide attempt among Chinese rural residents. J Affect Disord 2019;246:465–73. https://doi.org/10.1016/j.jad.2018.12.111.Search in Google Scholar PubMed PubMed Central

26. Schmidhuber, J. Deep learning in neural networks : an overview. Neural Netw 2015;61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003.Search in Google Scholar PubMed

27. Fujita, O. Trial-and-Error correlation learning. IEEE Trans Neural Netw 1993;4:720–2. https://doi.org/10.1109/72.238327.Search in Google Scholar PubMed

28. Lo, JT-H. Convexification for data fitting. J Global Optim 2010;46:307–15. https://doi.org/10.1007/s10898-009-9417-z.Search in Google Scholar

29. Yang, W, Liu, X, Wang, K, Hu, J, Geng, G, Feng, J. Sex determination of three-dimensional skull based on improved backpropagation neural network. Comput Math Methods Med 2019;2019:9163547. https://doi.org/10.1155/2019/9163547.Search in Google Scholar PubMed PubMed Central

30. Du, KL, Swamy, MNS. Neural networks in a softcomputing framework. Neural Netw Softcomput Framew 2006:1–566.Search in Google Scholar

31. Wang, J, Yang, J, Wu, W. Convergence of cyclic and almost-cyclic learning with momentum for feedforward neural networks. IEEE Trans Neural Netw 2011;22:1297–306. https://doi.org/10.1109/tnn.2011.2159992.Search in Google Scholar

32. Toulon, P, Berruyer, M, Lasne, D, Telion, C, Arcizet, J, Giacomello, R, et al.. Results of a multicentre study aimed at defining the age-specific reference ranges. Thromb Haemost 2016.Search in Google Scholar

33. Ho, P, Ng, C, Rigano, J, Tacey, M, Smith, C, Donnan, G, et al.. Significant age, race and gender differences in global coagulation assays parameters in the normal population. Thromb Res 2017;154:80–3. https://doi.org/10.1016/j.thromres.2017.04.009.Search in Google Scholar PubMed


Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2021-0081).


Received: 2021-01-18
Accepted: 2021-02-15
Published Online: 2021-03-03
Published in Print: 2021-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Scroll Up Arrow