Psychiatry

Prediction of suicide using web based voice recordings analyzed by artificial intelligence.

The integration of machine learning (ML) and deep learning models in suicide risk assessment has advanced significantly in recent years. In this study, we utilized ML in a case-control design, we predicted completed suicides using publicly available, web-based, real-world voice data, and treating speech as a biomarker. Our model demonstrated high accuracy in distinguishing between individuals who ...

Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.

Machine Learning (ML) is a promising approach for predicting outcomes of youth anxiety treatments. To this end, data from nine randomized controlled trials of youth anxiety treatments were concatenated into a dataset (N = 1362; M = 10.59, SD = 2.47; 48.9% female; 71.9% White, 5.9% Black, Other, 5.9%; 10.8% Hispanic) and ML algorithms were used to predict outcomes. Models were then applied on an ex...

Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disea...

Evaluating the Quality of Psychotherapy Conversational Agents: Framework Development and Cross-Sectional Study.

BACKGROUND: Despite potential risks, artificial intelligence-based chatbots that simulate psychotherapy are becoming more widely available and frequen...

Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.

BACKGROUND: The development of automatic emotion recognition models from smartphone videos is a crucial step toward the dissemination of psychotherape...

Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.

OBJECTIVE: Depression in older adults is a growing public health concern, yet there is still a lack of convenient and real-time methods for depressive...

Crisis-line workers' perspectives on AI in suicide prevention: a qualitative exploration of risk and opportunity.

BACKGROUND: Crisis support services offer crucial intervention for individuals in acute distress, providing timely access to trained volunteers whose ...

Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation.

Anxiety and depression disorders are increasingly common, necessitating methods for real-time assessment and early identification. This study investig...

Exploring the potential of lightweight large language models for AI-based mental health counselling task: a novel comparative study.

In recent years, Transformer-based large language models (LLMs) have significantly improved upon their text generation capability. Mental health is a ...

Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study.

This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using...

Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.

Autism spectrum disorder (ASD) is a neurodevelopmental condition especially in children with a strong hereditary basis, making its early diagnosis cha...

A deep learning approach to stress recognition through multimodal physiological signal image transformation.

Stress is widely acknowledged as a significant contributor to health issues. Recognizing stress involves assessing an individual's physiological and p...

Depression detection methods based on multimodal fusion of voice and text.

Depression is a prevalent mental health disorder, and early detection is crucial for timely intervention. Traditional diagnostics often rely on subjec...

Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.

Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and act...

Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.

Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and...

Predicting autism from written narratives using deep neural networks.

Despite the heterogeneity of language and communication abilities within the autistic population, challenges associated with the pragmatic (social) us...

An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube comments.

Online videos are vital for health education and medical decision-making, but their comment sections often spread misinformation, causing anxiety and ...

Analyzing mental stress in Indian students through advanced machine learning and wearable technologies.

Mental stress is a prevalent issue in modern society, and detecting and classifying it accurately is crucial for effective interventions and treatment...

Machine learning-based model for behavioural analysis in rodents applied to the forced swim test.

The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressi...

Plasma Lyso-PE 22:6 and Lyso-PE 20:4 are associated with development of mild to moderate depression revealed by metabolomics: a pilot study.

BACKGROUND: Mild to moderate depression (MMD), as an early stage of depression, has a high incidence and may progress to severe depression, even leadi...