Comparison of neural networks and statistical models to predict gestational age at birth

Eastaugh, J.; Smye, S W; Snowden, S.; Walker, James J.; Dear, P. R. F.; Farrin, Amanda

doi:10.1007/bf01413827

Cited by 3 publications

(1 citation statement)

References 19 publications

(17 reference statements)

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“…The model reported by Misra et al [3] , which included biomedical and psychosocial factors but not serum biomarkers, did not find maternal education to be a predictor of PTB. Two additional studies that used maternal characteristics but not biomarkers in their models calculated areas under the ROC curve of 0.70-0.73 [23,24] . The multivariate model reported by Smith et al [17] , which took into account two biomarkers ( ␣ -fetoprotein and human chorionic gonadotropin) and maternal sociodemographic characteristics (but not specifically maternal education) yielded an area under the ROC curve of 0.62 for prediction of delivery at 33-36 weeks.…”

Section: Discussionmentioning

confidence: 99%

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

Pearce

Grove²,

Bonney³

et al. 2010

Gynecol Obstet Invest

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Background/Aims: To examine the relationship of biological mediators (cytokines, stress hormones), psychosocial, obstetric history, and demographic factors in the early prediction of preterm birth (PTB) using a comprehensive logistic regression model incorporating diverse risk factors. Methods: In this prospective case-control study, maternal serum biomarkers were quantified at 9–23 weeks’ gestation in 60 women delivering at <37 weeks compared to 123 women delivering at term. Biomarker data were combined with maternal sociodemographic factors and stress data into regression models encompassing 22 preterm risk factors and 1st-order interactions. Results: Among individual biomarkers, we found that macrophage migration inhibitory factor (MIF), interleukin-10, C-reactive protein (CRP), and tumor necrosis factor-α were statistically significant predictors of PTB at all cutoff levels tested (75th, 85th, and 90th percentiles). We fit multifactor models for PTB prediction at each biomarker cutoff. Our best models revealed that MIF, CRP, risk-taking behavior, and low educational attainment were consistent predictors of PTB at all biomarker cutoffs. The 75th percentile cutoff yielded the best predicting model with an area under the ROC curve of 0.808 (95% CI 0.743–0.874). Conclusion: Our comprehensive models highlight the prominence of behavioral risk factors for PTB and point to MIF as a possible psychobiological mediator.

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Section: Discussionmentioning

confidence: 99%

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

Pearce

Grove²,

Bonney³

et al. 2010

Gynecol Obstet Invest

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A Computational Intelligence Approach for Ranking Risk Factors in Preterm Birth

Zaharie

Holban

Lungeanu

et al. 2007

2007 4th International Symposium on Applied Computational Intelligence and Informatics

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Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors

Mitra

Dunson²

2010

The International Journal of Biostatistics

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Stochastic search variable selection (SSVS) algorithms provide an appealing and widely used approach for searching for good subsets of predictors while simultaneously estimating posterior model probabilities and model-averaged predictive distributions. This article proposes a two-level generalization of SSVS to account for missing predictors while accommodating uncertainty in the relationships between these predictors. Bayesian approaches for allowing predictors that are missing at random require a model on the joint distribution of the predictors. We show that predictive performance can be improved by allowing uncertainty in the specification of predictor relationships in this model. The methods are illustrated through simulation studies and analysis of an epidemiologic data set.

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Comparison of neural networks and statistical models to predict gestational age at birth

Cited by 3 publications

References 19 publications

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

A Computational Intelligence Approach for Ranking Risk Factors in Preterm Birth

Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors

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