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机器学习在血液透析患者死亡率预测中的应用。

Machine learning approach in mortality rate prediction for hemodialysis patients.

机构信息

Electrical Engineering Department, University of Montenegro, Podgorica, Montenegro.

Clinic for Nephrology, Clinical Center of Montenegro, Podgorica, Montenegro.

出版信息

Comput Methods Biomech Biomed Engin. 2022 Jan;25(1):111-122. doi: 10.1080/10255842.2021.1937611. Epub 2021 Jun 14.

Abstract

Kernel support vector machine algorithm and -means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

摘要

采用核支持向量机算法和 - 均值聚类算法来确定血液透析患者的预期死亡率。利用黑山国家肾病数据库进行了这项研究。当观察完整数据库时,死亡率预测的准确率高达 94.12%;当观察简化数据库(仅包含三种最常见基本疾病的数据)时,准确率高达 96.77%。此外,结果表明,仅需少数几个参数(其中大多数在单次患者检查中收集),就足以获得满意的结果。

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