Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Naqvi, Syed Asil Ali; Tennankore, Karthik; Vinson, Amanda J.; Roy, Patrice

doi:10.2196/26843

Cited by 29 publications

(19 citation statements)

References 45 publications

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“…The research extends to the prediction of graft survival approach by proposing a new three-phase approach, that is, (i) data processing phase, (ii) feature selection phase, and (iii) prediction phase [ 23 ].…”

Section: Proposed Ab-ann Methodologymentioning

confidence: 99%

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Chawla

Balaji²,

Alabdali

et al. 2022

Journal of Healthcare Engineering

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A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.

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Section: Proposed Ab-ann Methodologymentioning

confidence: 99%

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Chawla

Balaji²,

Alabdali

et al. 2022

Journal of Healthcare Engineering

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“…Various related work showed that AI approaches based on medical input data can result in accurate and robust statistical models to predict patient outcomes [30][31][32][33][34]. For example, supervised tree-based ML algorithms, for example, RF and Extreme Gradient Boosting, have shown promising results for classification tasks for posttransplant risks, for example, for graft failure, patient survival, or graft loss within a certain time period [35][36][37][38][39][40]. However, most of the existing approaches were research-driven and had only limited access to real-world medical data for their work, for example, using the Scientific Registry of Transplant Recipients data set [41].…”

Section: Using ML For Cpmsmentioning

confidence: 99%

NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

Schapranow,

Bayat,

Rasheed

et al. 2023

JMIR Res Protoc

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Background Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. Objective The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. Methods To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. Results We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. Conclusions For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. International Registered Report Identifier (IRRID) DERR1-10.2196/48892

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“…Thus, existing risk communication tools such as [10] identify survival functions obtained from the Cox PH model at these time points. Many previous publications follow this classification approach [3, 11, 12]. However, this approach requires to train a model for each time point.…”

Section: Introductionmentioning

confidence: 99%

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models

Salaün,

Knight,

Wingfield

et al. 2023

Preprint

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The decision to accept an organ offer for transplant, or wait for something potentially better in the future, can be challenging. Especially, clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretable methods to predict both graft failure and patient death using data from previously accepted kidney transplant offers. Precisely, using more than twenty years of transplant outcome data, we train and compare several survival analysis and classification models in both single and multiple risk settings. In addition, we use post hoc interpretability techniques to clinically validate these models. In a single risk setting, neural networks provide comparable results to the Cox proportional hazard model, with 0.71 and 0.81 AUROC for predicting graft failure and patient death at year 10, respectively. Recipient and donor ages, primary renal disease, donor eGFR, donor type, and the number of mismatches at DR locus appear to be important features for transplant outcome prediction. We also extended the neural network approach to multiple outcome prediction, maintaining consistent performances and clinical interpretation. Thus, owing to their good predictive performance and the clinical relevance of their post hoc interpretation, neural networks represent a promising core component in the construction of future decision support systems for transplant offering.

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Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Cited by 29 publications

References 45 publications

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network

NephroCAGE—German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models

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