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Related Concept Videos

Regression Toward the Mean01:52

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 researchers try to extrapolate results...

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Machine Learning for Predicting Stillbirth: A Systematic Review.

Qingyuan Li1, Pan Li2, Junyu Chen3

  • 1Department of Clinical Medicine, International Medical College of Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China.

Reproductive Sciences (Thousand Oaks, Calif.)
|July 30, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) show promise in predicting stillbirth, a major global health concern. While current models demonstrate accuracy, further development is needed for clinical application.

Keywords:
Machine learningPredictionPregnant womenStillbirth

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Area of Science:

  • Reproductive Health
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Stillbirth affects over 5 million pregnancies annually, posing a significant global health challenge.
  • The complex, multifactorial nature of stillbirth complicates accurate prediction and prevention efforts.
  • Artificial intelligence (AI) and machine learning (ML) offer potential solutions for enhancing clinical decision-making in stillbirth risk assessment.

Purpose of the Study:

  • To systematically review existing literature on machine learning (ML) models developed for stillbirth prediction.
  • To analyze the characteristics of input data, performance metrics, and validation methods of these predictive models.
  • To identify the current state and limitations of AI-driven stillbirth prediction.

Main Methods:

  • A comprehensive literature search was conducted across PubMed, Cochrane, and Web of Science databases for studies utilizing AI in stillbirth prediction.
  • Qualitative analysis, including narrative synthesis and graphical representation, was employed to analyze findings.
  • Risk of bias and applicability were assessed using PROBAST, with a focus on model design and performance.

Main Results:

  • Eight studies, encompassing 14,840,654 women, were included. Common algorithms were neural networks, random forests, and logistic regression, using 14-53 predictive features.
  • Model validation was limited, with only 50% of studies performing it and 25% conducting external validation.
  • Area Under the Curve (AUC) ranged from 0.54-0.9, with sensitivity and specificity varying. A stacked ensemble model achieved AUC=0.9 and >85% sensitivity/specificity.

Conclusions:

  • Machine learning models show considerable accuracy in predicting stillbirth, with potential to aid clinical decision-making.
  • Despite promising results, current ML models require further refinement and robust validation before widespread clinical implementation.
  • Future research should focus on improving model generalizability and external validation to ensure reliable clinical utility.