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Regression Toward the Mean

Regression Toward the Mean

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...

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Updated: Jul 26, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Identifying risk of stillbirth using machine learning.

Tess E K Cersonsky¹, Nina K Ayala¹, Halit Pinar²

¹Department of Obstetrics & Gynecology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI.

American Journal of Obstetrics and Gynecology

|June 14, 2023

Summary

This summary is machine-generated.

Machine learning models accurately predict stillbirth risk using pregnancy data, identifying 85% of cases before viability. These advanced tools offer improved risk stratification for better clinical decision-making and stillbirth prevention.

Keywords:

boosted trees clinical decision-making factor analysis maternal serum alpha-fetoprotein)prenatal care previability random forests second-trimester prenatal screen (Down syndrome risk structural racism ultrasound unconjugated estriol

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Area of Science:

Perinatal Medicine
Machine Learning in Healthcare
Predictive Analytics

Background:

Traditional logistic regression models for stillbirth prediction lack the sophistication to capture complex, nonlinear relationships.
Advanced machine learning (ML) methods offer superior capabilities for modeling intricate associations between variables and stillbirth outcomes.

Purpose of the Study:

To develop and optimize ML models for predicting stillbirth using data available early in pregnancy (22-24 weeks) and throughout gestation.
To identify key demographic, medical, and prenatal factors, including ultrasound and fetal genetics, that contribute to stillbirth risk.

Main Methods:

Secondary analysis of the Stillbirth Collaborative Research Network (SCRN) dataset (2006-2009).
Development of random forests models using data available before viability and throughout pregnancy.
Identification and assessment of variable importance for stillbirth prediction.

Main Results:

A random forests model using previability data achieved 85.1% accuracy, with high sensitivity (88.6%) and specificity (85.3%).
A model incorporating data throughout pregnancy showed 85.0% accuracy, with 92.2% sensitivity and 77.9% specificity.
Key predictors included prior stillbirth, minority race, early gestational age at visit/ultrasound, and second-trimester screening.

Conclusions:

Sophisticated ML models can accurately predict stillbirth risk (85%) before viability using comprehensive clinical data.
Validated ML models hold potential for effective risk stratification and clinical decision support in identifying and monitoring high-risk pregnancies.