Depression

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care. The US Department of Veteran Affairs' suicide prediction model primarily leverages structured electronic health records (EHR) data. This approach largely overlooks unstructured EHR, a data format that could be utilized to enhance predictive accurac...

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatmen...

Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to devel...

[From AI to polygenic risk scores: which innovations will shape the future of psychiatry?].

BACKGROUND: In recent years, developments have been made in various research domains, from treatments with (es)ketamine to large-scale genome-wide ass...

Optimizing Speech-Input Length for Speaker-Independent Depression Classification

Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression cl...

GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study

Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can ...

Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models f...

Context-Aware Deep Learning for Multi Modal Depression Detection

In this study, we focus on automated approaches to detect depression from clinical interviews using multi-modal machine learning (ML). Our approach ...

Robust Speech and Natural Language Processing Models for Depression Screening

Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening...

Detecting anxiety and depression in dialogues: a multi-label and explainable approach

Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakehold...

Multi-atlas Ensemble Graph Neural Network Model For Major Depressive Disorder Detection Using Functional MRI Data

Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. I...

Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to ...

Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism

Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall p...

CodoMo: Python Model Checking to Integrate Agile Verification Process of Computer Vision Systems

Model checking is a fundamental technique for verifying finite state concurrent systems. Traditionally, model designs were initially created to faci...

Investigation of in vitro neuronal activity processing using a CMOS-integrated ZrO2-based memristive crossbar

The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was stu...

Investigating Acoustic-Textual Emotional Inconsistency Information for Automatic Depression Detection

Previous studies have demonstrated that emotional features from a single acoustic sentiment label can enhance depression diagnosis accuracy. Additio...

Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

Mental health disorders are increasingly prevalent worldwide, creating an urgent need for innovative tools to support early diagnosis and interventi...

Depression detection from Social Media Bangla Text Using Recurrent Neural Networks

Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. T...

If Eleanor Rigby Had Met ChatGPT: A Study on Loneliness in a Post-LLM World

Loneliness, or the lack of fulfilling relationships, significantly impacts a person's mental and physical well-being and is prevalent worldwide. Pre...