Medicine and Health Sciences Commons^™

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

The Influence Of Unpredictable, Fragmented Parental Signals On The Developing Brain, Laura M. Glynn, Tallie Z. Baram

Psychology Faculty Articles and Research

Mental illnesses originate early in life, governed by environmental and genetic factors. Because parents are a dominant source of signals to the developing child, parental signals - beginning with maternal signals in utero - are primary contributors to children’s mental health. Existing literature on maternal signals has focused almost exclusively on their quality and valence (e.g. maternal depression, sensitivity). Here we identify a novel dimension of maternal signals: their patterns and especially their predictability/unpredictability, as an important determinant of children’s neurodevelopment. We find that unpredictable maternal mood and behavior presage risk for child and adolescent psychopathology. In experimental models, fragmented/unpredictable …