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Salehnasab, Cirruse, Hajifathali, Abbas, Asadi, Farkhondeh, Parkhideh, Sayeh, Kazemi, Alireza, Roshanpoor, Arash, Mehdizadeh, Mahshid, Tavakoli-Ardakani, Maria, Roshandel, Elham. (1400). An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation. سامانه مدیریت نشریات علمی, 11(3), 345-356. doi: 10.31661/jbpe.v0i0.2012-1244
Cirruse Salehnasab; Abbas Hajifathali; Farkhondeh Asadi; Sayeh Parkhideh; Alireza Kazemi; Arash Roshanpoor; Mahshid Mehdizadeh; Maria Tavakoli-Ardakani; Elham Roshandel. "An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation". سامانه مدیریت نشریات علمی, 11, 3, 1400, 345-356. doi: 10.31661/jbpe.v0i0.2012-1244
Salehnasab, Cirruse, Hajifathali, Abbas, Asadi, Farkhondeh, Parkhideh, Sayeh, Kazemi, Alireza, Roshanpoor, Arash, Mehdizadeh, Mahshid, Tavakoli-Ardakani, Maria, Roshandel, Elham. (1400). 'An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation', سامانه مدیریت نشریات علمی, 11(3), pp. 345-356. doi: 10.31661/jbpe.v0i0.2012-1244
Salehnasab, Cirruse, Hajifathali, Abbas, Asadi, Farkhondeh, Parkhideh, Sayeh, Kazemi, Alireza, Roshanpoor, Arash, Mehdizadeh, Mahshid, Tavakoli-Ardakani, Maria, Roshandel, Elham. An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation. سامانه مدیریت نشریات علمی, 1400; 11(3): 345-356. doi: 10.31661/jbpe.v0i0.2012-1244

An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation

Journal of Biomedical Physics and Engineering
مقاله 10، دوره 11، شماره 3، مرداد و شهریور 2021، صفحه 345-356    اصل مقاله (1.07 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2012-1244
نویسندگان
Cirruse Salehnasab1؛ Abbas Hajifathali2؛ Farkhondeh Asadi* 1؛ Sayeh Parkhideh2؛ Alireza Kazemi1؛ Arash Roshanpoor3؛ Mahshid Mehdizadeh2؛ Maria Tavakoli-Ardakani2؛ Elham Roshandel4
1PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2MD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3PhD, Department of Computer Science, Sama Technical and Vocational Training College, Tehran Branch (Tehran), Islamic Azad University (IAU), Tehran, Iran
4PhD, Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: Acute graft-versus-host disease (aGvHD) is a complex and often multisystem disease that causes morbidity and mortality in 35% of patients receiving allogeneic hematopoietic stem cell transplantation (AHSCT).
Objective: This study aimed to implement a Clinical Decision Support System (CDSS) for predicting aGvHD following AHSCT on the transplantation day.
Material and Methods: In this developmental study, the data of 182 patients with 31 attributes, which referred to Taleghani Hospital Tehran, Iran during 2009–2017, were analyzed by machine learning (ML) algorithms which included XGBClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, and RandomForestClassifier. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, and specificity. Using the machine learning developed model, a CDSS was implemented. The performance of the CDSS was evaluated by Cohen’s Kappa coefficient.
Results: Of the 31 included variables, albumin, uric acid, C-reactive protein, donor age, platelet, lactate Dehydrogenase, and Hemoglobin were identified as the most important predictors. The two algorithms XGBClassifier and HistGradientBoostingClassifier with an average accuracy of 90.70%, sensitivity of 92.5%, and specificity of 89.13% were selected as the most appropriate ML models for predicting aGvHD. The agreement between CDSS prediction and patient outcome was 92%.
Conclusion: ML methods can reliably predict the likelihood of aGvHD at the time of transplantation. These methods can help us to limit the number of risk factors to those that have significant effects on the outcome. However, their performance is heavily dependent on selecting the appropriate methods and algorithms. The next generations of CDSS may use more and more machine learning approaches.
کلیدواژه‌ها
Graft vs Host Disease؛ Decision Support Systems, Clinical؛ Machine learning؛ Classification
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