一种新的预产前早期预测方法,解决由于临床数据集中缺少数据导致的预测不确定性
Jin Woo Kim1, Nari Kim2, Ju Yeon Kim1
1Smart MEC Healthcare R&D Center, CHA Bundang Medical Center, Gyeonggi-do, Republic of Korea.
Scientific reports
|February 12, 2026
概括
一个新的框架通过量化由于缺少数据而导致的预测不确定性,准确地预测高风险母亲的孕前 (PE). 这种机器学习方法提高了诊断可靠性,减少了对预测的过度信心.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 孕前 (PE) 对母亲的健康构成重大风险.
- 早期检测和干预对于高风险妊娠至关重要.
- 由于数据不确定性,现有的预测模型可能缺乏可靠性.
研究的目的:
- 开发一个机器学习框架,用于预测早产子前.
- 为了整合一个不确定性得分,反映缺少的临床数据.
- 提高孕妇PE风险评估的可靠性和信心.
主要方法:
- 利用了 31,235 个单独怀孕的多中心回顾性临床数据集.
- 开发了一种机器学习模型,其中包含了沙普利增量解释 (SHAP) 值.
- 基于缺失的数据贡献和在不同不确定性值下评估的性能,量化预测不确定性.
主要成果:
- 该框架在低不确定性值下实现了高预测性能 (内部AUROC 0.978,外部AUROC 0.994).
- 与不考虑不确定性的模型相比,AUROC显著改善 (0.845内部,0.693外部).
- 在不确定性值和预测性表现之间观察到强烈的反相关性 (斯皮尔曼的rho: -0.999).
结论:
- 开发的框架提供了一个稳定和有效的方法,用于早期预测产前.
- 考虑到数据不确定性,可以提高预测可靠性,减少过度自信.
- 这种方法提供了一个更可靠的工具,用于识别高风险的孕前的母亲.
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