Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Long-term Depression

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...

Depressive Disorders: Etiology

Depressive Disorders: Etiology

Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...

Depression: Overview

Depression: Overview

Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...

Antidepressant Drugs: MAOIs and Other Agents

Antidepressant Drugs: MAOIs and Other Agents

Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Capturing omega sign in the clinical assessment of depression by deep learning.

Translational psychiatry·2026

Same author

Melanopsin-Mediated Post-Illumination Pupillary Response in Idiopathic Rapid Eye Movement (REM) Sleep Behavior Disorder and Parkinson's Disease.

Movement disorders : official journal of the Movement Disorder Society·2026

Same author

Associations between wearable-device-measured daytime and nighttime light exposures and dementia risk: A prospective cohort study.

General psychiatry·2026

Same author

Mindfulness-Based Psychoeducation App to Improve the Well-Being of Parents and Caregivers of Children With Autism: Development and Usability Study.

JMIR pediatrics and parenting·2026

Same author

Association of Prodromal Parkinson's Disease-Like Features in Long COVID With Dream-Enactment Behaviours.

Journal of sleep research·2026

Same author

Higher Daytime Light Exposure Predicts Lower Risk of Gastrointestinal Cancer Incidence and Mortality.

International journal of cancer·2026

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine

Tim M H Li¹, Jie Chen¹, Framenia O C Law¹

¹Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).

JMIR Medical Informatics

|December 6, 2023

Summary

This summary is machine-generated.

Analyzing patient speech with natural language processing (NLP) and machine learning (ML) can detect suicide risk in depression, even when patients deny suicidal ideation. This technology offers a promising tool for more accurate and objective suicide risk assessment.

Keywords:

automated detection clinical interview depression machine learning natural language processing suicidal ideation

More Related Videos

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Psychiatry
Computational Linguistics
Machine Learning

Background:

Assessing suicide risk is challenging, particularly when patients deny suicidal ideation.
Primary care providers show limited agreement in screening suicide risk.
Patients' speech patterns may offer objective, language-based indicators of suicidal ideation.

Purpose of the Study:

To determine if suicidal ideation can be detected using language features from clinical depression interviews via natural language processing (NLP) and machine learning (ML).

Main Methods:

A cross-sectional study of 305 participants (depression and healthy controls) using structured interviews (Hamilton Depression Rating Scale).
Suicide risk was clinician-rated; interviews were transcribed and analyzed using Linguistic Inquiry and Word Count (LIWC).
Ordinal logistic regression and random forest models were employed to analyze language features and detect suicide risk.

Main Results:

Significant suicide-related language features were identified; increased use of anger words correlated with higher suicide risk (OR 2.91, P=.02).
Random forest models effectively identified high suicide risk (AUC 0.76-0.89) and general suicide risk (AUC 0.83-0.92) through text analysis.
Satisfactory suicide risk detection was achieved even without explicit disclosure of suicidal ideation.

Conclusions:

NLP and ML analysis of clinical interview texts show potential for accurate and specific suicidality detection.
Findings may lead to high-performance, automated suicide risk assessment tools, including chatbot-based screening.
Language-based analysis offers a novel approach to identifying suicide risk in individuals with depression.