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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.

Cheyenne Mangold1, Sarah Zoretic2, Keerthi Thallapureddy1

  • 1Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA.

Neonatology
|July 14, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) models show promise in accurately predicting neonatal mortality. Future research should prioritize external validation and calibration for clinical application.

Keywords:
Artificial intelligenceMortalityNeonateSystematic review

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • Neonatal mortality remains a critical global health challenge, with nearly half of all under-five child deaths occurring in the first month of life.
  • Identifying neonates at high risk is crucial for targeted interventions and improving global child survival rates.
  • Artificial intelligence (AI) offers potential for early identification of modifiable risk factors in neonatal mortality.

Purpose of the Study:

  • To systematically review and analyze studies utilizing AI for predicting neonatal mortality.
  • To identify commonly used AI models, predictors, and performance metrics in neonatal mortality prediction.
  • To assess the quality of evidence regarding AI-driven neonatal mortality prediction.

Main Methods:

  • A comprehensive literature search was conducted across major databases (PubMed, Cochrane, OVID, Google Scholar).
  • Studies employing AI, including machine learning (ML) and deep learning, for neonatal death prediction were included; studies with <500 participants or using only antenatal factors were excluded.
  • Data extraction focused on study design, AI models, features, validation methods, and performance metrics (AUC, sensitivity, specificity).

Main Results:

  • Eleven studies involving 1.26 million neonates were included, with predictions made from 5 minutes to 7 days of life.
  • Neural networks, random forests, and logistic regression were common AI models; Area Under the Curve (AUC) ranged from 58.3% to 97.0%.
  • While models showed varying accuracy (sensitivity 63-80%, specificity 78-99%), only a minority reported external validation or calibration.

Conclusions:

  • AI, particularly ML models, demonstrates significant potential for accurate prediction of neonatal mortality.
  • This review highlights prevalent AI predictors and evaluation metrics, informing future model development.
  • Further research must emphasize external validation, robust calibration, and the development of accessible clinical applications for AI-driven neonatal care.