一个更高性能的算法用于识别从纵向儿科医疗记录中不合理的生长数据
Kylie K Harrall1,2, Sarah M Bird3,4, Keith E Muller5
1Department of Health Outcomes and Biomedical Informatics, University of Florida School of Medicine, Gainesville, FL, USA. KylieHarrall@ufl.edu.
Scientific reports
|August 6, 2024
概括
准确的儿科体重指数 (BMI) 追踪对于预测慢性疾病风险至关重要. 新的开源算法有效地识别和删除儿童不合理的身高和体重测量,改善增长轨迹分析的数据质量.
科学领域:
- 儿科生长监测 儿科生长监测
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 追踪儿科身体尺寸轨迹,如体重指数 (BMI),对于评估慢性疾病风险至关重要.
- 电子医疗记录中不准确的身高和体重测量可以导致生物学上不合理的数据,使生长轨迹分析复杂化.
- 目前用于识别错误的儿科生长数据的现有方法可能不是最佳的.
研究的目的:
- 开发和验证开源算法来检测和删除生物学上不合理的儿科身高和体重测量.
- 为了提高儿童体质指数 (BMI) 轨迹建模的准确性.
- 加强对暴露,BMI轨迹和随后的健康状况之间的关联分析.
主要方法:
- 开发新的开源算法,用于识别儿科身高和体重数据中的不可思议值.
- 开发的算法与使用蒙特卡洛模拟的现有三种已发表的算法进行比较.
- 基于灵敏度,特异性和速度的算法性能评估,使用从纵向流行病学队列的模拟输入.
主要成果:
- 新开发的算法与之前发布的三种算法相比,具有更高的特异性.
- 新算法的灵敏度和速度与现有方法相美.
- 这些发现表明,在清理儿科纵向生长数据方面,性能优越.
结论:
- 开发的开源算法有效地检测和删除生物学上不合理的儿科生长数据.
- 采用这些算法可以提高纵向儿科生长数据集的质量和可靠性.
- 改进的数据质量有助于更准确地建模BMI轨迹及其对健康的影响.
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