在儿童移植后早期预测生长模式,基于与高度相关的单核酸多形态
Yi Feng1, Yonghua Feng1, Mingyao Hu1
1Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China.
Chinese medical journal
|September 6, 2023
概括
机器学习使用遗传和临床数据准确预测移植后儿童的生长模式. 这种工具有助于管理生长迟缓和优化移植后护理,以改善结果.
科学领域:
- 儿科脏病学 儿科脏病学
- 遗传学 遗传学 是一个
- 机器学习 机器学习
背景情况:
- 增长迟缓是患有末期脏疾病 (ESRD) 的儿童的重大并发症.
- 移植可以部分改善生长,但预测移植后的生长仍然具有挑战性.
- 基因组和临床因素在移植后的生长轨迹中发挥着作用.
研究的目的:
- 开发和验证儿科ESRD患者移植后生长模式的预测模型.
- 利用集成基因组和临床变量的机器学习算法,以提高预测准确度.
- 为指导临床管理策略提供一个工具,以减轻生长迟缓.
主要方法:
- 对110名儿科移植接受者的回顾性队列研究,用于模型开发.
- 整体外体序列 (WES) 和729个与高度相关的单核酸多态 (SNP) 的收集.
- 应用七个机器学习算法,具有十倍的交叉验证,包括随机森林,用于模型构建和验证.
主要成果:
- 结合年龄和19个SNP的预测模型表现出强的表现.
- 随机森林模型在初级队列中实现了0.8125的准确性和0.924的AUC.
- 该模型显示出良好的外部验证,准确度为0.7949和AUC为0.796.
结论:
- 一个强大的机器学习模型被开发和验证,用于预测儿童的移植后生长.
- 该模型整合了单核酸多态 (SNP) 和临床数据,以有效预测生长模式.
- 这种预测工具可以帮助临床医生定制生长激素治疗和营养支持等干预措施,以改善儿科ESRD患者的生长结果.
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