A Study on the Artificial Intelligence Model of White Blood Cell Counts Prediction Using Gan

Authors

  • Han Ju-Hyuck Ph.D. Course, Department of Medical Engineering, Konyang University
  • Kim Yong-Suk Professor, Department of Medical Artificial Intelligence, Konyang University

Keywords:

WBC, Sepsis, GAN, Artificial Intelligence, Prediction Model

Abstract

In this paper, an artificial intelligence model for predicting white blood cell counts that required conventional blood tests were studied using deep learning. White blood cell counts are crucial human information for knowing inflammatory levels in the body and septic shock. However, white blood cell counts require a blood test, and two hours of medical waiting time is required to confirm the test results. Therefore, emergency patients may find it difficult to receive a sufficient medical response while waiting for blood test results that can reliably confirm white blood cell counts. As a process to solve this problem, medical responses are performed based on other bio-signals and information in the medical field. However, responses differ from responses according to quantitatively provided white blood cell counts. Therefore, in this study, we conducted a study on an artificial intelligence model that predicts white blood cell counts by receiving patient bio-signals and information based on Generative Adversarial Networks (GAN) of artificial intelligence. The artificial intelligence model of this study learns non-missing data and quantitatively predicts when the data with missing white blood cell counts act as input. The verification of the model consisted of the performance when input into the artificial intelligence model by mixing the original data and missing data. The verification results showed that the mixed data group, including white blood cell counts generated through the white blood cell prediction model, had better detection performance for sepsis patients than the original data group.

Downloads

Download data is not yet available.

References

Smith, T. J. Bohlke, K. Lyman, G. H. Carson, K. R. Crawford, J. Cross, S. J. & Armitage, J. O. (2015). Recommendations for the use of WBC growth factors: American Society of Clinical Oncology clinical practice guideline update. Journal of Clinical Oncology, 33(28), 3199-3212.

Singer, M. Deutschman, C. S. Seymour, C. W. Shankar-Hari, M. Annane, D. Bauer, M. & Angus, D. C. (2016). The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama, 315(8), 801-810. DOI: 10.1001/jama.2016.0287.

Wai, A. K. (2012). Tintinalli’s emergency medicine: a comprehensive study guide 7th ed. DOI: 10.1097/MEJ.0b013e328355386b

Landry, D. W. Levin, H. R. Gallant, E. M. Ashton, R. C. Seo, S. D'Alessandro, D. & Oliver, J. A. (1997). Vasopressin deficiency contributes to the vasodilation of septic shock. Circulation, 95(5), 1122-1125.

Chiu, C. & Legrand, M. (2021). Epidemiology of sepsis and septic shock. Current Opinion in Anesthesiology, 34(2), 71-76.

Ge, Y. L. Liu, C. H. Rana, M. A. Zhu, X. Y. Wang, N. Xu, J. & Wang, H. Y. (2019). Elevated Red Blood Cell Distribution Width Combined White Blood Cell in Peripheral Blood Routine Have a Better Sensitivity than CURB-65 Scores in Predicting ICU Admission and Mortality in Adult Community-Acquired Pneumonia Patients. Clinical laboratory, 65(3).

Simionato, G. Hinkelmann, K. Chachanidze, R. Bianchi, P. Fermo, E. van Wijk, R. & Quint, S. (2021). Red blood cell phenotyping from 3D confocal images using artificial neural networks. PLoS computational biology, 17(5), e1008934.

Goldstein, L. N. Wells, M. & Vincent-Lambert, C. (2019). The cost of time: A randomised, controlled trial to assess the economic impact of upfront, point-of-care blood tests in the Emergency Centre. African Journal of Emergency Medicine, 9(2), 57-63.

Evans, L. Rhodes, A. Alhazzani, W. Antonelli, M. Coopersmith, C. M. French, C. & Levy, M. (2021). Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive care medicine, 47(11), 1181-1247.

Egger, M. Ley, M. & Hanke, S. (2019). Emotion recognition from physiological signal analysis: A review. Electronic Notes in Theoretical Computer Science, 343, 35-55.

Hu, Z. Wang, H. Xie, J. Zhang, J. Li, H. Liu, S. & Huang, Y. (2021). Burnout in ICU doctors and nurses in mainland China–a national cross-sectional study. Journal of critical care, 62, 265-270.

Zhu, B. Feng, X. Jiang, C. Mi, S. Yang, L. Zhao, Z. & Zhang, L. (2021). Correlation between white blood cell count at admission and mortality in COVID-19 patients: a retrospective study. BMC Infectious Diseases, 21(1), 1-5.

Kumar, R., Joshi, S., & Dwivedi, A. (2021). CNN-SSPSO: a hybrid and optimized CNN approach for peripheral blood cell image recognition and classification. International Journal of Pattern Recognition and Artificial Intelligence, 35(05), 2157004.

Lu, Y., Qin, X., Fan, H., Lai, T., & Li, Z. (2021). WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Applied Soft Computing, 101, 107006.

Lin, S. F., Lin, H. A., Pan, Y. H., & Hou, S. K. (2022). A novel scoring system combining Modified Early Warning Score with biomarkers of monocyte distribution width, white blood cell counts, and neutrophil-to-lymphocyte ratio to improve early sepsis prediction in older adults. Clinical Chemistry and Laboratory Medicine (CCLM).

Reyna, M. Josef, C. Jeter, R. Shashikumar, S. Moody, B. Sharma, A. Nemati, S. & Clifford, G. (2019). Early Prediction of Sepsis from Clinical Data - the PhysioNet Computing in Cardiology Challenge 2019. https://physionet.org/content/challenge-2019/1.0.0/

Reyna, M. A. Josef, C. Seyedi, S. Jeter, R. Shashikumar, S. P. Westover, M. B. & Clifford, G. D. (2019). Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019. In 2019 Computing in Cardiology (CinC), IEEE.

Akkas, F. Sam, E. Cinislioglu, E. Karabulut, I. Yılmazel, F. K. Sunger, U. & Atar, F. A. (2021). The effect of the duration between preoperative bladder urine culture and semirigid ureteroscopic lithotripsy on the rate of systemic inflammatory response syndrome postoperatively. Urology Open A Open J, 2(2), 65-69.

Chakraborty, R. K. & Burns, B. (2019). Systemic inflammatory response syndrome. PMID: 31613449

Han, J. H. Park, G. H. & Kim, Y. S. (2020). An Early Predictive Model of Sepsis with White Blood Cell Calibration, TEST Engineering&Management_2020, 4135-4143.

Data Screening Process.

Downloads

Published

13.02.2023

How to Cite

Ju-Hyuck, H. ., & Yong-Suk, K. . (2023). A Study on the Artificial Intelligence Model of White Blood Cell Counts Prediction Using Gan. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 165–173. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2584

Issue

Vol. 11 No. 4s (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.