从使用Gompertz模型的胎儿生物识别来准确预测出生体重
Chandrani Kumari1,2, Gautam I Menon1,2,3, Leelavati Narlikar4
1The Institute of Mathematical Sciences, Chennai, India.
概括
一个新的Gompertz模型使用早期超声波生物识别准确预测胎儿体重. 这种机器学习方法达到8%的误差,优于晚期超声波方法,可以更好地监测胎儿的生长.
科学领域:
- 孕产妇和胎儿的医学
- 生物识别仪表是如何使用的
- 增长建模的发展模式.
背景情况:
- 精确的胎儿生长监测和出生体重估计是关键的临床实践.
- 目前的方法依赖于超声波生物识别,但缺乏一个简单的,基于预测生长模型的公式.
- 现有的出生体重估计取决于晚期超声波数据.
研究的目的:
- 使用Gompertz模型建模胎儿生物成长.
- 开发一种机器学习模型,根据Gompertz参数来预测出生体重.
- 与现有方法相比,评估模型的预测准确度.
主要方法:
- 使用了来自"Seethapathy队列" (774名孕妇) 的超声波生物识别测量.
- 应用了Gompertz模型,一个受约束的生长模型,来分析胎儿生物识别.
- 在推断的Gompertz参数上训练了一种机器学习模型,以预测出生体重 (BW).
主要成果:
- 戈珀茨模型表明,它非常适合胎儿生物特征的生长.
- 两个Gompertz参数似乎是普遍的,而第三个则捕获了个体胎儿尺度.
- 该ML模型预测出生体重的误差为8%,优于晚期超声波方法,并在独立队列中实现了8.4%的误差.
结论:
- 戈珀茨模型有效地适应胎儿生物识别的增长,并使得出生体重可以在没有晚期超声波的情况下进行估计.
- 该模型的单一尺度参数 () 解释了大多数个体变化,表明它对未来增长标准的实用性.
- 这种方法为早期和准确的胎儿生长评估提供了重要的临床价值.
相关概念视频
01:14Parametric Survival Analysis: Weibull and Exponential Methods
367
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
367
01:17z Scores and Area Under the Curve
10.4K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
10.4K
01:11Normal Distribution
10.6K
The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
10.6K
01:17Wald-Wolfowitz Runs Test II
190
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
190
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
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
29
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
29