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Selected morphotic parameters differentiating ulcerative colitis from crohn’s disease

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Języki publikacji
EN
Abstrakty
EN
This paper presents a method that binds statistical and data mining techniques, which aims to support the decision-making process in selected diseases of the digestive system. Currently, there is no precise diagnosis for ulcerative colitis (UC) and Crohn's disease (CD). Specialist physicians must exclude many other diseases occurring in the colon. The first goal of this study is a retrospective analysis of medical data of patients hospitalised in the Department of Gastroenterology and Internal Diseases, Bialystok, and finding the symptoms differentiating the two analysed diseases. The second goal is to build a system that clearly points to one of the two diseases UC or CD, which shortens the time of diagnosis and facilitates the future treatment of patients. The work focuses on building a model that can be the basis for the construction of action rules, which are one of the basic elements in the medical recommendation system. Generated action rules indicated differentiating factors, such as mean corpuscular volume, platelets (PLTs), neutrophils, monocytes, eosinophils, basophils, alanine aminotransferase (ALAT), creatinine, sodium and potassium. Other important parameters were smoking and blood in stool.
Rocznik
Strony
249--253
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
  • Faculty of Mechanical Engineering, Institute of Biomedical Engineering, Bialystok Technical University, ul. Wiejska 45C, 15-351 Bialystok, Poland
Bibliografia
  • 1. Bebas E., Borowska M., Derlatka M., Oczeretko E., Hladunski M., Szumowski P., Mojsak M. (2021) Machine-learning-based classification of the histological subtype o non-small-cell lung cancer using MRI texture analysis. Biomedical Signal Processing and Control, vol. 66, 1-6.
  • 2. Cappello, M.; Morreale, G.C. (2016) The Role of Laboratory Tests in Crohn’s Disease. Clin Med Insights Gastroenterol, 9, 51–62.
  • 3. Crohn B.B., Ginzburg L., Oppenheimer G.D. (1932) Regional ileitis. A pathologic and clinical entity, J Am Med Ass. 99:1323-1329.
  • 4. Daniluk J, Daniluk U, Reszec J, Rusak M, Dabrowska M, Dabrowski A.(2017) Protective effect of cigarette smoke on the course of dextran sulfate sodium-induced colitis is accompanied by lymphocyte subpopulation changes in the blood and colon. Int J Colorectal Dis, 32, 1551-1559.
  • 5. Dardzinska A. (2013), Action Rules Mining. Springer, pp. 90.
  • 6. Dardzinska A., Kasperczuk A. (2018), Decision-making Process in Colon Disease and Crohn’s Disease Treatment, Acta Mechanica et Automatica, Vol. 12 no. 3, pp. 227-231.
  • 7. Dardzinska A., Romaniuk A. (2016), Mining of Frequent Action Rules, Machine Intelligence and Big Data in Industry: 6th International Conference on Pattern Recognition and Machine Intelligence, 87-95 .
  • 8. Dolapcioglu, C.; Soylu, A.; Kendir, T.; Ince, A.T.; Dolapcioglu, H.; Purisa, S.(2014) Coagulation parameters in inflammatory bowel disease. Int J Clin Exp Med , 7, 1442–1448.
  • 9. Giuffrida, P.; Corazza, G.R.; Di Sabatino, A. (2018) Old and New Lymphocyte Players in Inflammatory Bowel Disease. Dig Dis Sci, 63, 277-288.
  • 10. Gren, S.T.; Grip, O. (2016) Role of Monocytes and Intestinal Macrophages in Crohn's Disease and Ulcerative Colitis. Inflamm Bowel Dis, 22, 1992-8.
  • 11. Gürdal O., Dardzinska A. (2017), A New Approach to Clinical Medicine by Action Rules, International Journal of Development Research, 7(1), 11032–11039.
  • 12. Han J., Kamber M. (2006), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Second Edition, 21-27.
  • 13. Kasperczuk A, Daniluk J, Dardzinska A. (2019) Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis. Appl. Sci, 9, 1650.
  • 14. Kasperczuk A. and Dardzinska A., (2016), Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database, Acta Mechanica et Automatica, Vol. 10 no. 3, pp. 233-238.
  • 15. Kirsner J. B. (1988), Historical aspects of inflammatory bowel disease, J Clin Gastroenterol ,10:286-297.
  • 16. Merigo, F.; Brandolese, A.; Facchin, S.; Missaggia, S.; Bernardi, P.; Boschi, F.; et al. (2018) Glucose transporter expression in the human colon. World J Gastroenterol, 24,775-793.
  • 17. Priyamvada, S.; Gomes, R.; Gill, R.K.; Seksena, S.; Alrefai, W.A.; Dudeja, P.K. (2015) Mechanisms Underlying Dysregulation of Electrolyte Absorption in IBD Associated Diarrhea. Inflamm Bowel Dis, 21, 2926–2935.
  • 18. Ras Z., Dardzinska A. (2011), From Data to Classification Rules and Action,. International Journal of Intelligent Systems, Wiley, 26(6), 572-590.
  • 19. Sarfati, M.; Wakahara, K.; Chapuy, L.; Delespesse, G. ( 2015) Mutual Interaction of Basophils and T Cells in Chronic Inflammatory Diseases. Front Immunol, 6, 399.
  • 20. Schieffer, K.M.; Bruffy, S.M.; Rauscher, R.; Koltun, W.A.; Yochum, G.S.; Gallagher, C.G. (2017) Reduced total serum bilirubin levels are associated with ulcerative colitis. PLoS One, 12, e0179267.
  • 21. Yazici, A.; Senturk, O.; Aygun, C.; Celebi, A.; Caglayan, C.; Hulagu, S. (2010) Thrombophilic Risk Factors in Patients with Inflammatory Bowel Disease. Gastroenterology Res., 3, 112–119.
  • 22. Zho, G.X.; Liu, Z.J. (2017) Potential roles of neutrophils in regulating intestinal mucosal inflammation of inflammatory bowel disease. J Dig Dis, 495-503.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3c2b1542-4789-4bcf-a54d-8641b6fbe34e
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