In Journal of biomedical physics & engineering
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.
Salehnasab Cirruse, Hajifathali Abbas, Asadi Farkhondeh, Parkhideh Sayeh, Kazemi Alireza, Roshanpoor Arash, Mehdizadeh Mahshid, Tavakoli-Ardakani Maria, Roshandel Elham
** Classification, Decision Support Systems, Clinical, Graft vs Host Disease, Machine learning**