Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant
| Autor: | Magdalena Krajewska, Jakub Stojanowski, Klaudia Rydzyńska, Andrzej Konieczny, Mariusz Kusztal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Artificial neural network business.industry delayed-graft function kidney transplantation General Medicine Perceptron medicine.disease artificial intelligence Kidney transplant Article Delayed Graft Function Weight difference deceased donors Random forest machine learning Internal medicine medicine Cardiology Medicine Risk assessment business Kidney transplantation |
| Zdroj: | Journal of Clinical Medicine, Vol 10, Iss 5244, p 5244 (2021) Journal of Clinical Medicine Volume 10 Issue 22 |
| ISSN: | 2077-0383 |
| Popis: | Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes. |
| Databáze: | OpenAIRE |
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