INTRODUCTION In many parts of the developing world procurement of antenatal gestational age estimates is not possible, challenging provision of appropriate perinatal care. This study aimed to develop a model for postnatal gestational age estimation utilizing measures of the newborn hemoglobin levels and other metabolic analyte data derived from newborn blood spot samples. METHODS We conducted...

Height-diameter models are classically analyzed by fixed or mixed linear and non-linear regression models. In order to possess the among-plot variability, we propose stochastic differential equations that are deduced from the standard deterministic dynamic ordinary differential equations by adding the process variability to the growth dynamic. The advantage of the stochastic differential equati...

BACKGROUND Bronchopulmonary dysplasia (BPD) is a commonly used outcome for randomized neonatal trials. OBJECTIVES The aim of the present study was to determine whether a diagnosis of BPD or respiratory morbidity (RM1 or RM2) at 12 months corrected age better predicted subsequent RM in extremely low gestational age infants (23-28 weeks of gestation). METHODS Initial analysis was undertaken i...

INTRODUCTION In combination with multivariate pattern analysis algorithms such as Support Vector Machines (SVM) [1] or Support Vector Regression (SVR) [2], functional connectivity offers a powerful tool for brain classification. With this approach, several groups have successfully identified and predicted brain characteristics using resting-state MRI data [35]. Specifically, Dosenbach et al. ha...

This paper explores the capabilities of text-based age and gender prediction geared towards the application of detecting harmful content and conduct on social media. More specifically, we focus on the use case of detecting sexual predators who try to “groom” children online and possibly provide false age and gender information in their user profiles. We perform age and gender classification exp...

OBJECTIVE This study examined the age-associated rate of incomplete mammograms requiring additional testing based on Breast Imaging-Reporting and Data System (BIRADS) score. METHODS A retrospective, observational study design from a tertiary medical center was used to evaluate which explanatory variables significantly predicted whether a woman had an incomplete mammogram. An incomplete mammog...

Principal Components Analysis is a dimensionality reduction technique that determines eigenvectors (principal components) and corresponding eigenvalues from the covariance matrix of a data set. The eigenvectors that do not contribute much to scatter are truncated without excessive data loss. The remaining eigenvectors represent a new coordinate system in lower dimensional space, allowing a more...