LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,113
تعداد مقالات10,208
تعداد مشاهده مقاله40,770,405
تعداد دریافت فایل اصل مقاله8,800,178
Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. (1402). Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia. سامانه مدیریت نشریات علمی, 14(3), 378-385. doi: 10.30476/mejc.2022.94116.1715
Mostafa Langarizadeh; Boshra Farajollahi; Abbas Hajifathali. "Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia". سامانه مدیریت نشریات علمی, 14, 3, 1402, 378-385. doi: 10.30476/mejc.2022.94116.1715
Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. (1402). 'Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia', سامانه مدیریت نشریات علمی, 14(3), pp. 378-385. doi: 10.30476/mejc.2022.94116.1715
Langarizadeh, Mostafa, Farajollahi, Boshra, Hajifathali, Abbas. Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia. سامانه مدیریت نشریات علمی, 1402; 14(3): 378-385. doi: 10.30476/mejc.2022.94116.1715

Presenting a Prediction Model for Successful Allogenic Hematopoietic Stem Cell Transplantation in Adults with Acute Myeloid Leukemia

Middle East Journal of Cancer
مقاله 4، دوره 14، شماره 3 - شماره پیاپی 55، مهر 2023، صفحه 378-385    اصل مقاله (860.95 K)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/mejc.2022.94116.1715
نویسندگان
Mostafa Langarizadeh1؛ Boshra Farajollahi* 1؛ Abbas Hajifathali2
1Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran
2Hematopoietc Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed to implement different models of data mining (DM) (single and ensemble) for prediction of allogenic hematopoietic stem cell transplantation in patients with acute myeloid leukemia (transplantation against host disease).
Method: We conducted this developmental study on 94 patients with 34 attributes in Taleghani Hospital, Tehran, Iran, during 2009–2017. In this practical study, data were analyzed via decision tree (DT) algorithms, including decision tree, random forest and gradient boosting (ensemble learning), artificial neural network (Single Learning), and support vector machine. Some criteria, like specificity, accuracy, Fmeasure, AUC (area under curve), and sensitivity, were reported in order to evaluate DT algorithms.
Results: There were 34 transplantation-related variables; some predictors, such as liver, hemoglobin, and donor blood group, were found to be the most important ones. To predict aGVHD, the two selected algorithms included the most appropriate DM models, artificial neural network and support vector machine classifiers, with ROC of 100.
Conclusion: This study indicated that DT algorithms could be successfully used for approving the efficiency of the models predicting allogenic hematopoietic stem cell transplantation.

تازه های تحقیق
Mostafa Langarizadeh (Google Scholar)
Boshra Farajollahi (Google Scholar)
کلیدواژه‌ها
Data mining؛ Allogenic hematopoietic stem cell transplantation؛ Leukemia؛ Myeloid؛ Acute
مراجع
  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7-34. doi:10.3322/caac.21551.
  2. Dhillon A, Singh A. Machine learning in healthcare data analysis: A survey. J Biol Today's World. 2018;8(2):1-10. doi: 10.15412/J.JBTW.01070206.
  3. Chu Y, Chen F, Sheng Z, Zhang D, Zhang S, Wang W, et al. Blood cancer diagnosis using ensemble learning based on a random subspace method in laser induced breakdown spectroscopy. Biomed Opt Express. 2020;11(8):4191-202. doi: 10.1364/BOE.395332.
  4. Mousavinasab SN, Yazdani Cherati J, Karami H, Khaksar S. Risk Factors influencing the survival of pediatric acute leukemia using competing risk model. [In Persian] J Mazandaran Univ Med Sci. 2015;24(121):31-8.
  5. Bittencourt MC, Ciurea SO. Recent advances in allogeneic hematopoietic stem cell transplantation for acute myeloid leukemia. Biol Blood Marrow Transplant. 2020;26(9):e215-e21. doi: 10.1016/j.bbmt.2020.06.007.
  6. England JT, Saini L, Hogge D, Forrest D, Narayanan S, Power M, et al. Day 14 Bone marrow evaluation during acute myeloid leukemia induction in a real world Canadian cohort. Clin Lymphoma Myeloma Leuk. 2020;20(7):e427-e36. doi: 10.1016/j.cmlm.2020.02.012.
  7. Shallis RM, Wang R, Davidoff A, Ma X, Zeidan AM. Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood Rev. 2019;36:70-87. doi: 10.1016/j.blre.2019.04.005.
  8. Doppelhammer M, Fraccaroli A, Prevalsek D, Bucklein V, Habe S, Schulz C, et al. Comparable outcome after haploidentical and HLA-matched allogeneic stem cell transplantation for high-risk acute myeloid leukemia following sequential conditioning—a matched pair analysis. Ann Hematol. 2019;98(3):753-62. doi:10.1007/s00277-019-03593-2.
  9. Wang W, Stiehl T, Raffel S, Hoang VT, Hoffmann I, Poisa-Beiro L, et al. Reduced hematopoietic stem cell frequency predicts outcome in acute myeloid leukemia. Haematologica. 2017;102(9):1567. doi: 10.3324/haematol.2016.163584.
  10. Shouval R, Bonifazi F, Fein J, Boschini C, Oldani E, Labopin M, et al. Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92(5):429-34. doi: 10.1002/ajh.24677.
  11. Muhsen IN, Jagasia M, Toor AA, Hashmi SK. Registries and artificial intelligence: Investing in the future of hematopoietic cell transplantation. Bone Marrow Transplant. 2019;54(3):477-80. doi:10.1038/s41409-018-0327-x.
  12. Fuse K, Uemura S, Tamura S, Suwabe T, Katagiri T, Tanaka T, et al. Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach. Cancer Med. 2019;8(11):5058-67. doi: 10.1002/cam4.2401.
  13. Aytan P, Yeral M, Korur A, Gereklioglu C, Kasar M, Buyukkurt NH, et al. Factors associated with overall survival in acute myeloid leukemia patients before and after hematopoietic stem cell transplant. Exp Clin Transplant. 2021;19(8):856-64. doi: 10.6002/ect.2018.0352.
  14. Shouval R, Labopin M, Unger R, Giebel S, Ciceri F, Schmid C, et al. Prediction of hematopoietic stem cell transplantation related mortality-lessons learned from the in-silico approach: A European society for blood and marrow transplantation Acute Leukemia working party data mining study. PLoS One. 2016;11(3):e0150637. doi: 10.1371/journal.pone.0150637.
  15. Radakovich N, Cortese M, Nazha A. Acute myeloid leukemia and artificial intelligence, algorithms and new scores. Best Pract Res Clin Haematol. 2020;33(3):101192. doi: 10.1016/j.beha.2020.101192.
  16. Kashef A, Khatibi T, Mehrvar A. Treatment outcome classification of pediatric Acute Lymphoblastic Leukemia patients with clinical and medical data using machine learning: A case study at MAHAK hospital. Inform Med Unlocked. 2020;20:100399. doi: 10.1016/j.imu.2020.100399.
  17. Ghorbani R, Ghousi R. Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science. 2019;3(2):47-70. doi: 10.5267/j.ijdns.2019.1.003.
  18. Elsawy M, Sorror M. Up-to-date tools for risk assessment before allogeneic hematopoietic cell transplantation. Bone Marrow Transplant. 2016;51(10):1283-300. doi: 10.1038/bmt.2016.141.
  19. Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare (Basel). 2018;6(2):54. doi:10.3390/healthcare6020054.
  20. Potdar R, Varadi G, Fein J, Labopin M, Nagler A, Shouval R. Prognostic scoring systems in allogeneic hematopoietic stem cell transplantation: Where do we stand? Biol Blood Marrow Transplant. 2017;23(11):1839-46. doi: 10.1016/j.bbmt.2017.07.028.
  21. Salehnasab C, Hajifathali A, Asadi F, Parkhideh S, Kazemi A, Roshanpoor A, et al. An Intelligent Clinical Decision Support System for Predicting Acute Graftversus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation. J Biomed Phys Eng. 2021;11(3):345-56. doi: 10.31661/jbpe.v0i0.2012-1244.
  22. Arai Y, Kondo T, Fuse K, Shibasaki Y, Masuko M, Sugita J, et al. Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Blood Adv. 2019;3(22):3626-34. doi:10.1182/bloodadvances.2019000934.
  23. Lee C, Haneuse S, Wang HL, Rose S, Spellman SR, Verneris M, et al. Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation. PLoS One. 2018;13(1):e0190610. doi:10.1371/journal.pone.0190610.
  24. Okamura H, Nakamae M, Koh S, Nanno S, Nakashima Y, Koh H, et al. Interactive web application for plotting personalized prognosis prediction curves in allogeneic hematopoietic cell transplantation using machine learning. Transplantation. 2021;105(5):1090-6. doi:10.1097/TP.0000000000003357.
  25. Tang S, Chappell GT, Mazzoli A, Tewari M, Choi SW, Wiens J. Predicting acute graft-versus-host disease using machine learning and longitudinal vital sign data from electronic health records. JCO Clin Cancer Inform. 2020;4:128-35. doi: 10.1200/CCI.19.00105.
  26. Salehnasab C, Hajifathali A, Asadi F, Roshandel E, Kazemi A, Roshanpoor A. Machine learning classification algorithms to predict aGvHD following Allo-HSCT: A systematic review. Methods Inf Med. 2019;58(6):205-212. doi: 10.1055/s-0040-1709150.
آمار
تعداد مشاهده مقاله: 1,988
تعداد دریافت فایل اصل مقاله: 2,367


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب