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The utility of machine learning for predicting donor discard in abdominal transplantation.
Pettit, Rowland W; Marlatt, Britton B; Miles, Travis J; Uzgoren, Selim; Corr, Stuart J; Shetty, Anil; Havelka, Jim; Rana, Abbas.
Afiliação
  • Pettit RW; Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
  • Marlatt BB; Research and Development, InformAI, Houston, Texas.
  • Miles TJ; Department of Surgery, Division of Abdominal, Transplantation, Baylor College of Medicine, Houston, Texas, USA.
  • Uzgoren S; Research and Development, InformAI, Houston, Texas.
  • Corr SJ; Department of Cardiovascular Surgery, Houston Methodist Hospital, Houston, Texas, USA.
  • Shetty A; Department of Bioengineering, Rice University, Houston, Texas, USA.
  • Havelka J; Department of Biomedical Engineering, University of Houston, Texas, USA.
  • Rana A; Department of Medicine, Swansea University Medical School, Swansea, Wales, UK.
Clin Transplant ; 37(5): e14951, 2023 05.
Article em En | MEDLINE | ID: mdl-36856124
BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a balanced assessment of the potential for an organ to be used in a transplantation procedure. METHODS: We accessed and utilized all available deceased donor United Network for Organ Sharing data from 1987 to 2020. With these data, we evaluated the performance of multiple machine learning methods for predicting organ use. The machine learning methods trialed included XGBoost, random forest, Naïve Bayes (NB), logistic regression, and fully connected feedforward neural network classifier methods. The top two methods, XGBoost and random forest, were fully developed using 10-fold cross-validation and Bayesian optimization of hyperparameters. RESULTS: The top performing model at predicting liver organ use was an XGBoost model which achieved an AUC-ROC of .925, an AUC-PR of .868, and an F1 statistic of .756. The top performing model for predicting kidney organ use classification was an XGBoost model which achieved an AUC-ROC of .952, and AUC-PR of .883, and an F1 statistic of .786. CONCLUSIONS: The XGBoost method demonstrated a significant improvement in predicting donor allograft discard for both kidney and livers in solid organ transplantation procedures. Machine learning methods are well suited to be incorporated into the clinical workflow; they can provide robust quantitative predictions and meaningful data insights for clinician consideration and transplantation decision-making.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doadores de Tecidos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doadores de Tecidos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article