使用机器学习进行可解释的纵向预孕前风险预测
medRxiv : the preprint server for health sciences
|August 30, 2023
概括
这项研究开发了一种新的工具,用于纵向预测子宫前风险,比目前的方法识别了48.6%的风险患者. 这种人工智能驱动的方法增强了对孕妇的早期检测和个性化护理.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 孕前会影响2-8%的怀孕,并导致高达26%的孕产妇死亡.
- 目前的预测工具无法识别高达66%的受影响患者.
- 需要改进的工具来纵向预测子宫前风险,这是非常必要的.
研究的目的:
- 开发和验证一种用于纵向预测孕前风险的新型工具.
- 为了比较各种机器学习和深度学习模型的性能,用于预测产前.
- 调查预测模型中的可解释性和变量关系.
主要方法:
- 在八家医院对120752名患者进行了回顾性研究.
- 使用了社会人口统计,临床,家族史,实验室和生命体征数据.
- 在八个妊娠时间点开发和比较了线性回归,随机森林,xgboost和深度神经网络.
主要成果:
- 在研究人群中,孕前的发病率为5.7%.
- 模型性能 (AUC) 在0.73-0.91之间,经过外部验证.
- 这种新型的模型与第一季度的标准护理相比,发现了48.6%的风险患者.
结论:
- 一种新的纵向预孕前预测工具使用常规临床数据显示出高的预测能力.
- 该工具可以在整个怀孕期间进行早期识别和个性化风险评估.
- 在电子健康记录中的实施可以改善临床决策支持和围产期结果.
更多相关视频
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.1K
05:31Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles
Published on: January 26, 2024
861
相关概念视频
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:18Steps in Outbreak Investigation
152
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152