开发和验证基于索赔的算法,用于估计自发堕胎和终止妊娠年龄
Yanmin Zhu1, Sonia Hernandez-Diaz2, Brian T Bateman1,3
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
American journal of epidemiology
|September 24, 2024
概括
准确的妊娠年龄 (GA) 算法对于研究医疗保健数据中的自发流产 (SAB) 和终止风险至关重要. 一个经过验证的随机森林模型证明了在这些怀孕结果中估计GA的准确性有所提高.
科学领域:
- 生殖健康 生殖健康
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 医疗保健利用数据库对于研究诸如自发流产 (SAB) 和终止等怀孕结果是有价值的.
- 准确估计妊娠年龄 (GA) 对于可靠分析这些数据集内的SAB和终止风险至关重要.
- 现有的方法可能缺乏对怀孕结果进行可靠研究所需的精度.
研究的目的:
- 开发和验证使用医疗保健利用数据估计自发堕胎 (SAB) 和终止案件的妊娠年龄 (GA) 的算法.
- 为了比较GA估计的不同算法方法的性能.
- 评估使用行政医疗保健数据研究SAB和终止的可行性.
主要方法:
- 开发了一个分层算法来对医疗补助数据中的怀孕结果进行分类.
- 从链接的电子医疗记录中抽象GA作为黄金标准.
- 对比了三种GA估计方法:中位数归算,随机抽样和回归模型 (包括随机森林与Boruta特征选择).
主要成果:
- 与其他方法相比,随机森林回归模型 (方法3) 在平均平方误差 (MSE) 和R平方方面表现优越.
- 对于自发性流产 (SAB),58.0%的怀孕在黄金标准的2周内估计有GA (MSE:8.7,R-平方:0.09).
- 对于终止,66.3%的怀孕在黄金标准的2周内估计有GA (MSE:11.7,R平方:0.35).
结论:
- 经过验证的算法可以使用医疗保健利用数据来研究自发流产 (SAB) 和终止.
- 虽然可行,但对SAB和终止与活产相比,预计会有更高的妊娠年龄 (GA) 错误分类.
- 开发的随机森林模型在这些特定的怀孕结果中为GA估计提供了更好的准确性.
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