Machine learning support for kidney transplantation decision making

Reinaldo, Francisco; Rahman, Md. Anishur; Alves, Carlos F.; Malucelli, Andreia; Camacho, Rui

doi:10.1145/1722024.1722079

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…The RF training is computationally efficient, is robust to outliers and co-linearities, contains simple tuning parameters, and has demonstrated success for a variety of healthcare data applications [14,15]. RFs have also demonstrated utility in predicting deceased donor organ transplantation success and offer acceptances in simulated organ allocation models [3,4].…”

Section: Predictive Modelsmentioning

confidence: 99%

“…Recent advances in machine learning (ML) methods provide the opportunity to create highly nonlinear models with complex interactions among variables that may provide superior predictive power. Machine learning usage in medical domains is increasing as demonstrated by successful applications for predicting better utilization of perioperative antibiotics, predicting hospital lengths of stay, and indeed even recently in formulating alternative models for organ distribution and post-transplant implied utilization [1][2][3][4][5]. Despite evidence-based clinical and cost advantages of transplantation, nearly 1 in 5 viable deceased-donor kidneys procured are discarded (~4,000 per year) [6].…”

Section: Introductionmentioning

confidence: 99%

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A Predictive Model for Kidney Transplant Graft Survival using Machine Learning

Pahl¹,

Street²,

Johnson³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

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Section: Predictive Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Kidney Transplant Graft Survival using Machine Learning

Pahl¹,

Street²,

Johnson³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…21,22 RFs have also demonstrated utility in predicting deceased donor organ transplantation success and offer acceptances in simulated organ allocation models. 3,4 The contribution of each tree in an RF is similar to getting thousands of opinions based on a professional colleague's background. The clinical implementation of the RF algorithm works similarly to secondary and tertiary opinions among professionals in multiple specialties convening for the treatment of a complicated case.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…MLM usage in medical domains is increasing as demonstrated by successful applications for predicting better utilization of perioperative antibiotics, predicting hospital lengths of stay, and indeed even recently in formulating alternative models for organ distribution and post-transplant implied utilization. [1][2][3][4][5] Kidney transplantation is a cost-effective treatment for end-stage renal disease (ESRD) patients that provides a significant survival benefit and improves their quality of life compared to other forms of renal replacement; patients gain an estimated six quality-adjusted life years beyond other treatment methods. More than 600,000 ESRD patients in the US are on chronic dialysis therapy and the waitlist for life-saving kidney transplants numbers over 100,000 due to a shortage of identified, sufficiently low-risk, transplantable kidneys.…”

Section: Introductionmentioning

confidence: 99%

275 Predicting Deceased Donor Kidney Transplant Outcomes: Comparing KDRI/KDPI with Machine Learning

2019

American Journal of Kidney Diseases

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Background: Kidney transplantation is a cost-effective treatment for end-stage renal failure patients that provides a significant survival benefit and improves their quality of life compared to other forms of renal replacement. The predominant method used for donor kidney quality assessment is the Cox regression-based, piecewise linear kidney donor risk index (KDRI). A machine learning method (random forest) was compared to KDRI for predicting graft failure at 12, 24, and 36 months after transplantation. Methods: Random forest was trained and evaluated with the same deceased donor kidney transplant data (n=70242) initially used to develop KDRI (1995-2005) and included four readily available recipient variables from the estimated post-transplant survival score. Results: When comparing type II error rates of 10%, random forests predicted an additional 2,148 successful grafts at 36 months after transplant (126%) than KDRI. Many high-KDRI kidneys, at risk for discard, were correctly predicted for successful transplantation with random forests. Random forest performed significantly better than KDRI for graft Kaplan-Meier survival analysis from 0-240 months (log-rank test p<0.00). Conclusions: Machine learning methods can provide a significant improvement over KDRI for the assessment of kidney offers. This work lays the foundation for the use of machine learning methodologies in transplantation and describes the steps to measure, analyze, and validate future models.

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“…In 2010, Reinaldo et al [ 15 ] evaluated several simple and interpretable ML models, in which the decision tree model showed 94% accuracy in predicting graft survival 1 year after transplant.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Support for Decision-Making in Kidney Transplantation: Step-by-step Development of a Technological Solution

Paquette¹,

Ghassemi²,

Bukhtiyarova³

et al. 2022

JMIR Med Inform

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Background Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. Objective This study aimed to develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. Methods We used deidentified data on past organ donors, recipients, and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients. To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox proportional hazards), random survival forests, and several artificial neural networks (DeepSurv, DeepHit, and recurrent neural network [RNN]). We evaluated the performance of each model in terms of its ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were used: the C-index, integrated Brier score, and integrated calibration index, along with calibration plots. Results On the basis of the C-index metrics, the neural network–based models (DeepSurv, DeepHit, and RNN) had better discriminative ability than the Cox model and random survival forest model (0.650, 0.661, and 0.659 vs 0.646 and 0.644, respectively). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of technology readiness level 4. Conclusions Our technological solution based on the RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair.

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Machine learning support for kidney transplantation decision making

Cited by 5 publications

References 7 publications

A Predictive Model for Kidney Transplant Graft Survival using Machine Learning

A Predictive Model for Kidney Transplant Graft Survival using Machine Learning

275 Predicting Deceased Donor Kidney Transplant Outcomes: Comparing KDRI/KDPI with Machine Learning

Machine Learning Support for Decision-Making in Kidney Transplantation: Step-by-step Development of a Technological Solution

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