Depression

Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remi...

Speech-based Clinical Depression Screening: An Empirical Study

This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios, including psychiatric interviews, chatbot conversations, and text readings. Participants include depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital and control group members from the community, all diagnosed by psychiatrists follow...

Harmful Suicide Content Detection

Harmful suicide content on the Internet is a significant risk factor inducing suicidal thoughts and behaviors among vulnerable populations. Despite ...

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health rec...

[A research on depression recognition based on voice pre-training model].

For the increasing number of patients with depression, this paper proposes an artificial intelligence method to effectively identify depression throug...

Artificial Intelligence: A Game-Changer for Mental Health Care.

Starting from the escalating global burden of mental health disorders, exacerbated by the COVID-19 pandemic, the article examines the potential of art...

Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data.

BACKGROUND: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the dia...

Evaluating the Quality of Mental Health Information Generated by Large Language Model Chatbots

Artificial intelligence (AI) large language models (LLMs) hold great potential to transform psychiatry and mental health care by delivering relevant a...

A Natural Language Processing Pipeline based on the Columbia-Suicide Severity Rating Scale

Diagnostic codes in the Electronic Health Record (EHR) are known to be limited in reporting patient suicidality, and especially in differentiating the...

Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.

While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) model...

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.

Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly pres...

Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records.

In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compa...

Promoting Physical Activity Among Workers for Better Mental Health: An mHealth Intervention With Deep Learning.

There is clear scientific evidence that physical activity helps to prevent depression and anxiety. Utilizing mobile health (mHealth) technologies to e...

The New Emerging Treatment Choice for Major Depressive Disorders: Digital Therapeutics.

The chapter provides an in-depth analysis of digital therapeutics (DTx) as a revolutionary approach to managing major depressive disorder (MDD). It di...

Navigating the Intersection of Technology and Depression Precision Medicine.

This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include arti...

[A preliminary prediction model of depression based on whole blood cell count by machine learning method].

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping diffe...

Neuronetwork Approach in the Early Diagnosis of Depression.

BACKGROUND: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to qua...

ChatGPT, Artificial Intelligence, and Suicide Prevention.

Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate ...

Performance of Artificial Intelligence in Predicting Future Depression Levels.

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning mod...