Medicine and Health Sciences Commons^™

Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas

SMU Data Science Review

In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …

Socioemotional Selectivity And Psychological Health In Amyotrophic Lateral Sclerosis Patients And Caregivers: A Longitudinal, Dyadic Analysis, Suzanne C. Segerstrom, Edward J. Kasarskis, David W. Fardo, Philip M. Westgate

Psychology Faculty Publications

Objective: Socioemotional selectivity theory predicts that as the end of life approaches, goals and resources that provide immediate, hedonic reward become more important than those that provide delayed rewards. This study tested whether these goal domains differentially affected psychological health in the context of marital dyads in which one partner had been diagnosed with amyotrophic lateral sclerosis (ALS), a life-limiting disease.

Design: ALS patients (N = 102) being treated in three multidisciplinary clinics and their spouses (N = 100) reported their loneliness, financial worry and psychological health every 3 months for up to 18 months.

Main …

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) …

Depression, Sensation-Seeking Behavior And Violence As Mediators Of The Association Between Childhood Adversity And Substance Use Disorder, Calvin Wong

Legacy Theses & Dissertations (2009 - 2024)

Background: