Volume 21, Issue 74 (1-2019)
jha 2019, 21(74): 9-18 |
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Sobhkhizi A, Ashoori M. A Synthetic Data Mining Model for Evaluating Hypotension in Hemodialysis Patients. jha 2019; 21 (74) :9-18
URL: http://jha.iums.ac.ir/article-1-2771-en.html
URL: http://jha.iums.ac.ir/article-1-2771-en.html
1- School of Agriculture, Higher Educational Complex of Saravan, Saravan, Iran
2- 2. School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran; Corresponding author (mashoori@saravan.ac.ir ) ,maryam.ashoori@gmail.com
2- 2. School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran; Corresponding author (mashoori@saravan.ac.ir ) ,
Abstract: (4646 Views)
Introduction: Hypotension during Hemodialysis often increases mortality in patients undergoing dialysis for a long time. Hypotension is the most frequent adverse event during hemodialysis; therefore, the present study was conducted to investigate hypotension value of patients and present a predictive model using descriptive data mining.
Methods: In this cross-sectional study, conducted from May-June 2016, the data were extracted from Ali Ibn Abi Talib hospital in Zahedan and were then analyzed using Clementine 12.0. The model was presented using K-Means, C5.0 and CART algorithms.
Results: According to the findings the parameters influencing hypotension were buffer type and blood flow the importance of which was verified through clustering and the extracted rules from the model.
Discussion: The use of new modelling methods to analyze dialysis data and discover the existing relationships among them, changes the attitudes of dialysis personnel towards the process of dialysis and dialysis care. The evaluation of hypotension in hemodialysis patients helps a faster and more precise identification of hypotension. It would also facilitate proper and preventive management which enhances performance in dialysis centers. The study highlighted the importance of buffer type due to its effect on hypotension.
Methods: In this cross-sectional study, conducted from May-June 2016, the data were extracted from Ali Ibn Abi Talib hospital in Zahedan and were then analyzed using Clementine 12.0. The model was presented using K-Means, C5.0 and CART algorithms.
Results: According to the findings the parameters influencing hypotension were buffer type and blood flow the importance of which was verified through clustering and the extracted rules from the model.
Discussion: The use of new modelling methods to analyze dialysis data and discover the existing relationships among them, changes the attitudes of dialysis personnel towards the process of dialysis and dialysis care. The evaluation of hypotension in hemodialysis patients helps a faster and more precise identification of hypotension. It would also facilitate proper and preventive management which enhances performance in dialysis centers. The study highlighted the importance of buffer type due to its effect on hypotension.
Type of Study: Review |
Received: 2018/07/23 | Accepted: 2019/01/23 | Published: 2019/01/23
Received: 2018/07/23 | Accepted: 2019/01/23 | Published: 2019/01/23
References
1. Oshvandi K, Kavyannejad R, Borzuo S, Gholyaf M. Salavati M. Dialysis adequacy with high flux membrane in hemodialysis patients at Shahid Beheshti hospital, Hamedan. J Urmia Nurs Midwifery Fac. 2012; 10(4): 540-548. [In Persian]
2. Moslem A, Naghavi M, Basiri Moghadam M, Gharche M, Basiri Moghadam K. Assessing the adequacy of dialysis and its relationship with kind of filter in patient under hemodialysis referred to 22-Bahman hospital of Gonabad. Horizon Med Sci. 2008; 14(2): 20-23. [In Persian]
3. Tayyebi A, Shasti S, Ebadi A, Eynollahi B, Tadrisi S.D. The relationship between blood pressure and dialysis adequacy in dialysis patients. Crit Care Nurs J. 2012; 5(1): 49-52. [In Persian]
4. Makhlough A, Kashi Z. Assessment of the relationship between hypocalcaemia with hypotension. J Mazandaran Univ Med Sci. 2008; 18(64): 91-95. [In Persian]
5. Moattari M, Azarhooshang P, Abbasian A, ReisJalali G and Rajaeefard A. The effect of cool dialysate on intradialytic hypotension among end stage renal disease patients. Research in Medicine. 2007; 31(1): 67-72. [In Persian]
6. Goldsmith D, Covic A, Venning M and Ackrill P. Ambulatory blood pressure monitoring in renal dialysis and transplant patients.Am J Kidney Dis. 1997; 29(4): 593-600. [DOI:10.1016/S0272-6386(97)90343-7]
7. Wang Y.-F., Hu T.-M., Wu C.-C., Yu F.-C., Fu C.-M., Lin S.-H.,et al. Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network. Comput Methods Programs Biomed 2006; 83: 111-119. [DOI:10.1016/j.cmpb.2006.06.001]
8. Jooriyan N, Ashoori M. Predicting the effectiveness of preeclampsia medications based on dose and method of drug consumption using data mining. Iranian Journal of Obstetrics, Gynecology and Infertility. 2014; 17(123): 13-22. [In Persian]
9. Ashoori M, NajiMoghadam V, Alizadeh S, Safi M. Classification and clustering algorithm application for prediction of tablet numbers: case study diabetes disease. Health Inforamtion Management. 2013; 10(5): 739-749. [In Persian]
10. Ashoori M, Alizade S, Hosseiny Eivary HS, Rastad S, Hosseiny Eivary SS. A model to predict the sequential behavior of healthy blood donors using data mining. J Research Health. 2015; 5(2): 141-148.
11. Sepehri MM, Rahnama P, Shadpour P, Teimourpour B. A data mining based model for selecting type of treatment. Tehran Univ Med J. 2009; 67(6): 421-427. [In Persian]
12. Fernandez E A, Valtuille R, Willshaw P, Perazzo C. A. Dialysate-side urea kinetics. Neural network predicts dialysis dose during dialysis. Med Biol Eng Comput. 2003; 41(4): 392-396. [DOI:10.1007/BF02348080]
13. Kusiak A, Dixon B, Shah S. Predicting survival time for kidney dialysis patients: a data mining approach. Comput Biol Med. 2005; 35(4): 311-327. [DOI:10.1016/j.compbiomed.2004.02.004]
14. Yeh JY, Wu TH, Tsao CW. Using data mining techniques to predict hospitalization of hemodialysis patients. Decis Support Syst. 2011; 50(2): 439-448. [DOI:10.1016/j.dss.2010.11.001]
15. Tavousi A, Sepehri MM, Malakoutian T, Khatibi T. Data mining approach in prediction of erythropoietin dosage in hemodialysis patients. J Mazandaran Univ Med Sci. 2015; 25(129): 26-35. [In Persian]
16. Ashoori M. A model to predict hemodialysis buffer type using data mining techniques. Journal of Health Administration. 2017; 20(67): 99-110. [In Persian]
17. Bellazzi R, Larizza C, Magni P, Bellazzi R. Temporal data mining for the quality assessment of hemodialysis services. Artif Intell Med. 2005; 34(1): 25-39. [DOI:10.1016/j.artmed.2004.07.010]
18. Hekmat R, Mojahedi M and Garevani H. The effect of hemodialysis solution buffer on the control of blood pressure and efficacy of hemodialysis in chronic hemodialysis patients. Tehran Univ Med J. 2007; 65: 33-37.[In Persian]
19. López MI, Luna JM, Romero C, Ventura S. Classification via clustering for predicting final marks based on student participation in forum. In 5th International Conference on Educational Data Mining. 2012; 48–51.
20. Ashoori M, Mohammadi S, Hossieny Eivary H. Exploring blood donors' status through clustering: a method to improve the quality of services in blood transfusion centers. Journal of Knowledge & Health. 2016; 11(4): 73-82. [In Persian]
21. Rajabi J, Alizade S, Ashoori M. Identifying behavioral patterns in blood donation using K-means algorithm based on recency, frequency and blood value. Journal of Health Administration. 2018; 21(71): 66-78. [In Persian]
22. Ahmed HM, Aquina CT, Gracias VH. Damage control surgery, in robert wood johnson university hospital, New Brunswick, Springer 2012; 712-713.
23. Munger MA, Ateshkadi A, Cheung A K, Flaharty KK, Stoddard GJ, Marshall EH. Cardiopulmonary events during hemodialysis: effects of dialysis membranes and dialysate buffers. Am J Kidney Dis. 2000; 36(1): 130-139. [DOI:10.1053/ajkd.2000.8285]
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