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首页> 外文期刊>PLoS One >Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients
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Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients

机译：采用机器学习模型预测慢性肾病患者肾置换疗法的启动

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Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient’s well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient’s first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.
机译：在血液透析或肾移植的最佳时间内为慢性肾病（CKD）的患者开始肾置换疗法（RRT）对于患者的福祉以及成功的情况来说至关重要。在本文中，我们探讨了创建预测模型的可能性，以预测患者第一次诊断时间与CKD的第一次诊断，仅使用来自台湾国家健康保险的合并数据。本研究的目的是看看是否有限的数据（包括合并症，但不考虑在低收入国家获得昂贵的实验室值）可以为这种预测模型提供良好的基础。另一方面，在发达国家，这种模型可以允许政策制定者更好地规划和分配资源进行治疗。使用来自8,492名患者的数据，我们获得了0.773的接收器操作特征曲线（AUC）下的区域，以便在CKD诊断时预测RRT。结果还表明，在预测性能方面仅关注糖尿病患者没有额外的优势。虽然这些结果并不适合采用临床实践，但该研究提供了强有力的基础和各种方法，可以在医疗保健中的预测模型研究的未来研究。

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