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实验室测试结果的分类维护血液透析患者使用细胞生物电测量
Hanzhi Chen1, Leting Zhou1, Meilin Yan1
1Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, 214000, People's Republic of China.
International journal of general medicine
|September 2, 2024
概括
机器学习模型使用非侵入性生物电阻估计病患者的血清测试结果. 这种方法有助于监测健康状况,并减少不舒服的抽血的需要.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
背景情况:
- 末期病 (ESKD) 患者经历了贫血和营养不良等并发症.
- 目前的血清学测试是侵入性的和不频繁的,这给患者监测带来了挑战.
- 需要一种非侵入性的方法来评估血清学标记.
研究的目的:
- 开发机器学习模型来估计关键的血清学测试结果.
- 为此估计,使用非侵入性的细胞生物电阻测量.
- 为ESKD患者提供方便的监测工具.
主要方法:
- 员工支持矢量机器 (SVM) 和随机森林 (RF) 机器学习模型.
- 分类细胞的生物电学指标,以确定血清学测试结果.
- 通过688名患者的数据 (3872条记录) 验证模型.
主要成果:
- 两种SVM和RF模型都准确地分类了白蛋白,和副甲状腺激素水平 (低,正常,高).
- 与SVM相比,随机森林模型通常显示出更高的性能.
- 模型性能略有变化,SVM在女性中显示出更好的水平分类.
结论:
- 机器学习模型有效地使用生物电指标对血液透析患者的血清学测试结果进行了分类.
- 这种非侵入性方法可以通过电信号帮助评估物理化学指标.
- 这种方法有可能减少传统血清学检测的频率.
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