在移植手术中使用机器学习进行生存分析:实用入门。

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque
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引用次数: 0

摘要

背景:生存分析是移植研究的重要工具。机器学习技术的集成,特别是随机生存森林(RSF)模型,为预测建模和决策提供了潜在的增强。本研究旨在介绍RSF模型在肾移植生存分析中的应用,并为开发和评估预测算法提供实用指南。方法:采用RSF模型对肾移植受者的模拟数据集进行分析。使用分割样本(70%-30%)和交叉验证(5倍)技术将数据分为训练集、验证集和测试集,以评估模型性能。采用超参数整定策略选择最优模型。采用一致性指数(C-index)和综合Brier评分(IBS)进行内部验证。此外,还评估了随时间变化的AUC、F1评分、准确度和精度,以全面评估模型的预测性能。最后,拟合Cox比例风险模型,比较两种模型主要指标的结果。所有的分析都由分步代码支持,以确保再现性。结果:RSF模型的c指数为0.774,IBS为0.090。F1评分为0.945,准确度为89.67,精密度为90.99%。时间相关的ROC分析产生的AUC为0.709,表明预测性能中等。最后,分析表明三个最重要的变量是供体年龄、BMI和受体年龄。结论:本研究证明了RSF模型在肾移植分析中的稳健性和潜力,获得了强有力的验证指标,并突出了其在管理复杂、审查数据方面的优势,同时强调了进一步探索混合模型和临床整合的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survival analysis using machine learning in transplantation: a practical introduction.

Background: Survival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms.

Methods: We employed a RSF model to analyze a simulated dataset of kidney transplant recipients. The data were split into training, validation, and test sets using split sample (70%-30%) and cross-validation (5-folds) techniques to evaluate model performance. Hyperparameter tuning strategies were employed to select the best model. The concordance index (C-index) and Integrated Brier Score (IBS) were used for internal validation. Additionally, time-dependent AUC, F1 score, accuracy, and precision were evaluated to provide a comprehensive assessment of the model's predictive performance. Finally, a Cox Proportional Hazards model was fitted to compare the results of the main metrics between both models. All analyses were supported by step-by-step code to ensure reproducibility.

Findings: The RSF model obtained a C-index of 0.774, an IBS of 0.090. The F1 score was of 0.945, accuracy was 89.67 and precision was 90.99%. The time-dependent ROC analysis produced an AUC of 0.709, indicating a moderate predictive performance. Lastly, the analysis shows that the three most important variables are donor age, BMI, and recipient age.

Conclusions: This study demonstrates the robustness and potential of the RSF model in kidney transplant analysis, achieving strong validation metrics and highlighting its advantages in managing complex, censored data, while emphasizing the need for further exploration of hybrid models and clinical integration.

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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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