Martinino Alessandro, Khanolkar Ojus, Koyuncu Erdem, Petrochenkov Egor, Bencini Giulia, Olazar Joanna, Di Cocco Pierpaolo, Almario-Alvarez Jorge, Spaggiari Mario, Benedetti Enrico, Tzvetanov Ivo
Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
University of Illinois at Chicago College of Medicine, Chicago, Illinois, USA.
Int J Med Robot. 2024 Dec;20(6):e70035. doi: 10.1002/rcs.70035.
Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.
822 patients (OKT) and 169 (RKT) underwent kidney transplantation at our centre during the study period. A decision tree model was built in a two-step process consisting of: (1) Creating the model on the training data and (2) testing the predictive capabilities of the model using the test data.
Our model correctly predicted an OKT in 148 patients out of 161 test cases who received an OKT (accuracy 91%) and predicted an RKT in 19 out of 25 test cases of patients receiving an RKT (accuracy 76%).
Our model represents the inaugural data-driven model that furnishes concrete insights for the discernment between employing robotic and open surgery techniques.
机器学习已成为医疗保健领域的有力工具。构建了一个决策树模型，以改善在确定肾移植开放手术或机器人手术方法的最佳选择时的决策过程。
在研究期间，我们中心有822例患者（开放肾移植）和169例患者（机器人辅助肾移植）接受了肾移植。决策树模型通过两步过程构建，包括：（1）在训练数据上创建模型，（2）使用测试数据测试模型的预测能力。
我们的模型在161例接受开放肾移植的测试病例中，正确预测了148例（准确率91%），在25例接受机器人辅助肾移植的测试病例中，正确预测了19例（准确率76%）。
我们的模型是首个数据驱动的模型，为区分使用机器人手术和开放手术技术提供了具体见解。
