Author | Year | Objective | Population | Data source | Number of inputs in the final model | ML method | Validation method | Model performance | Result |
---|---|---|---|---|---|---|---|---|---|
Troiani and Carlin [29] | 2004 | Predict the incidence of disease after transplantation | 30 subjects | A database of home monitoring data | Six different ordinals symptom measures | Bayesian models, Markov chain Monte Carlo (MCMC) methods | Cross-validation | For the Bayesian model: ROC curve < 0.78, Sensitivity = 71.5, Specificity = 91.3 | Bayesian models have the best performance in comparison with the Markov model. |
Oztekin.A et al. [30] | 2009 | Predicting the graft survival | 16,604 cases | UNOS | 283 variables | decision trees, neural networks, logistic regression, Cox regression models | Ten-fold cross-validation, Confusion matrix, Sensitivity, Specificity, Accuracy | The accuracy ranged from 78–86% for logistic regression, from 79–86% for neural networks, and from71–79% for decision trees | The undiscovered relationships were founded among the survival-related variables. |
Delen.D et al. [31] | 2010 | Predict the risk factors for transplantation | 310,773 records and 565 variables | UNOS | 14 variables | SVM, ANN, MLP, RBF, DT (M5, CART), K-means | MSE, R2, 10-fold cross-validation, sensitivity analysis | SVM with an R2 value of 0.879, Neural network with an R2 value of 0.847 M5 algorithm-based regression tree model with R2 value of 0.785. | Thoracic organ recipients could be classified into ‘‘three’’ risk groups, namely low, medium, and high using a k-means clustering algorithm. |
Oztekin.A et al. [32] | 2011 | Predicting the performance of patients after transplantation | 16,771 records and 442 variables. | UNOS | 27 variables | Bayesian neural networks | 10-fold cross-validation, the R2 value of 0.68 | The R2 value of 0.73 | The ML models are superior to the existing techniques in terms of both prediction and interpretation capabilities. |
Hosseini-Baharanchi. F et al. [19] | 2016 | Predict Bronchiolitis Obliterans Syndrome incidence | 44 LTX recipients who survived ≥ 3 months post-LTX | Masih Daneshvari Hospital database | Five variables | Bayesian non-parametric model | Hazard ratio (HR), Monte Carlo error (MC-error) | MC-error values lower than 0.01 | Our analysis revealed that CMV infection was associated with a significant increase in the risk of developing BOS. |
Pande.A et al. [20] | 2017 | Predict pulmonary functions after transplantation | 9471 FEV1 evaluations were available from 509 LTx patients | Cleveland Clinic data | 17 variables | Gradient Boosting, generalized additive models (GAM), Boosted multivariate trees for spirometry data | cross-validation method | Standardized RMSE (sRMSE) averaged over 100 independent replications. | Developed models illustrate that forced 1-second lung expiratory volume (FEV1) has an important feature-time interaction for lung transplant patients. |
Barbosa.E et al. [33] | 2017 | Predict Bronchiolitis Obliterans Syndrome incidence | 176 LTx patients | Cardiothoracic clinic | The predictors were qCT metrics, PFTs, or SQS. | Multivariate logistic regression, SVM | The model’s prediction performance was assessed by AUC or Area under a ROC curve in cross-validated samples | Combination of MVLR and SVM based on PFT values: Max AUC 0.771, whereas models using qCT metrics-only outperformed models: max AUC 0.817 SVM models utilizing PC from qCT outperformed PFT (AUC = 0.817 vs. AUC = 0.767), | Combinations of qCT metrics with PFTs could predict BOS in the LTx group |
Oztekin.A et al. [22] | 2018 | Predict quality of life | 60,888 records and 443 features | UNOS | 147 input features | Genetic algorithm, GA-kNN, GA-ANN, and GA-SVM models | 5-fold cross-validation | Precisionclass1 = 0.992, Sensitivityclass1 = 0.998, Specificity Class1 = 0.996, F-Measure Class1 = 0.995, And G-Meanclass1 = 0.994, AUC = 85% | Applying GA-ANN, GA-kNN, and GA-SVM models proved that the performance of the lung transplantation process could be improved by the GASVM approach. |
Barbosa.E. et al. [21] | 2018 | Predict Bronchiolitis Obliterans Syndrome incidence | 71 LTx patients | Belgium clinic | 14 variables | Support vector machines (SVMs) | R2 score | Accuracies for SVM: 83%. Sensitivity of 73.3% and a specificity of 92.3% | ML utilization showed that qCT metrics predict the eventual onset of BOS. |
Mark.E et al. [13] | 2019 | Predict survival rate | 20 000 samples | UNOS | 128 variables | Linear regression, Cox proportional hazards model, Random Forest | 10-fold cross-validation | RMSE = 5.4, 9.0, and 5.3 for the heart, liver, and lung recipients | For all investigated organs, five-year survival was predicted for the majority of patients. |
Braccioni.F et al. [23] | 2020 | The role of CPET parameters in the development of respiratory symptoms in lung recipients | Twenty-four bilateral LTx recipients. | Tertiary teaching Hospital in Northeast Italy | Nine variables | Forest-Tree as ensemble-of-trees methods | 5-fold cross-validation | Bottom boxes, correlation matrix, coefficient score, and box plot for each split. | Muscle pain at peak exercise was associated with basal and exercise- metabolic altered pathways. Dyspnea was associated with the intensity of ventilatory response |
Dueñas-Jurado.J et al. [24] | 2021 | Predict recipient-donor matching | 404 lung transplants | Reina Sofı´a University Hospital | 48 variables | logistic regression (LR), product unit neural networks (PUNNs) | 10-fold cross-validation | Chi-Square, coefficient score, and correlation were investigated to estimate the developed models | The proposed models represent a powerful tool for donor-recipient matching. |
Shaish.H et al. [25] | 2021 | Predict survival rate | 221 CT images of ILD patients | Institutional radiology database | Five variables | CNN, univariable logistic regression model, Cox regression analysis | Five-fold cross-validation | AUC = 0.7417, Sensitivity = 77%, Specificity = 66% for the CNN model | Virtual lung wedge resection in patients with ILD can be used as an input to a CNN for predicting the histopathologic UIP pattern and transplant-free survival. |
Zafar.F et al. [28] | 2022 | Predict recipient-donor matching | 19,263 eligible double LTxs | UNOS | 43 variables | LASSO Cox regression, Random Forest tree, COX regression | Not mentioned | The covariate levels of each recipient and the adjusted total risk score was computed for every recipient and the density plot | LAPT could be effective in matching donor-recipient through lung transplantation. |
Su.J et al. [27] | 2022 | Determine the role of infection in rejection | 181 sputum samples from 59Â L | Guangzhou Medical University | 34 variables | Random Forest models | 10-fold cross-validation | AUC for the combination of procalcitonin (PCT), the six-genera, and T-lymphocyte levels were 0.919, 0.898, and 0.895 | Airway microbiota along with PCT and T lymphocyte levels were determined as predictive factors in infection and acute rejection. |
Stefanuto.P et al. [26] | 2022 | Predict primary graft dysfunction after lung transplantation | 35 lung transplants recipients | Harefield Hospital | 27 variables | Support vector machine (SVM) with a linear kernel, Multivariate analysis of variance (MANOVA) | Not mentioned | For SVM, AUROC = 0.90 and an accuracy of 0.83 | Three main chemical classes that were effective in PGD prediction were identified using model development. |