Psychiatric and Mental Health 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. …

Exploring Emotion Recognition For Vr-Ebt Using Deep Learning On A Multimodal Physiological Framework, Nicholas Dass

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality …

Near Real-Time Determination Of The Prevalence Of Cannabinoids, Cathinones, And Synthetic Opioids, Catherine O'Rourke, Bikram Subedi

Scholars Week

Unregulated new psychoactive substances (NPS) in “pure” or “preparation” forms are designed to mimic the effects of controlled substances, and are introduced and reintroduced in the market as a cheap substitute of established regulated drugs in quick succession to loophole the law enforcement efforts on combating drugs. For example, carfentanil, a synthetic opioid activates the opioid receptors similar to morphine but it is astoundingly potent compared to a typical drug of abuse (100 times more potent than fentanyl and 10,000 times more powerful than morphine). Based on the cost- and time-intensive forensic analysis, National Drug Early Warning System reported the …

Determination Of Cannabinoids, Cathinones, And Synthetic Fentanyls Using Wastewater-Based Epidemiology, Catherine O'Rourke, Bikram Subedi

Posters-at-the-Capitol

Unregulated new psychoactive substances (NPS) in “pure” or “preparation” forms are designed to mimic the effects of controlled substances, and are introduced and reintroduced in the market as a cheap substitute for established regulated drugs in quick succession to loophole the law enforcement efforts on combating drugs. For example, carfentanil, a synthetic opioid activates the opioid receptors similar to morphine but it is astoundingly potent compared to a typical drug of abuse (100 times more potent than fentanyl and 10,000 times more powerful than morphine). Based on the cost- and time-intensive forensic analysis, National Drug Early Warning System reported the …

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) …

A Deep Learning Approach To Diagnosing Schizophrenia, Justin Barry

Electronic Theses and Dissertations

In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning …

Comforting With Mathematics: A Case Study, Michael J. Goldstein

Journal of Humanistic Mathematics

Death by suicide often leaves behind grieving family members with unanswered questions. Of these concerns, fear that their loved one suffered or felt regret is common. When the method of suicide was jumping from height, that answer can easily be determined using basic kinematics. Despite the perception that mathematics is a cold, calculating field, it can provide a clear, definitive answer and comfort those left behind.

Reimagining Medical Education In The Age Of Ai, Steven A. Wartman, C. Donald Combs

Computational Modeling & Simulation Engineering Faculty Publications

Available medical knowledge exceeds the organizing capacity of the human mind, yet medical education remains based on information acquisition and application. Complicating this information overload crisis among learners is the fact that physicians' skill sets now must include collaborating with and managing artificial intelligence (AI) applications that aggregate big data, generate diagnostic and treatment recommendations, and assign confidence ratings to those recommendations. Thus, an overhaul of medical school curricula is due and should focus on knowledge management (rather than information acquisition), effective use of AI, improved communication, and empathy cultivation.