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Mental Health

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How do neighborhood environments impact adolescent health: a comprehensive study from subjective and objective perspectives using machine learning method.

Frontiers in public health
Existing studies have established a linear relationship between urban environments and adolescent health, but the combined impacts of subjective and objective environments on multi-dimensional health status (including physical and mental health) have...

Training humans to supplement a machine learning system: The role of guides in a digital mental health intervention.

Social science & medicine (1982)
Machine learning (ML) is increasingly prevalent in mental health care, with contemporary initiatives leveraging these technologies, sometimes in combination with wearable devices, toward novel interventions. This paper investigates the development of...

Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.

JMIR research protocols
BACKGROUND: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are...

Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

JMIR medical informatics
BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured ...

Responsible Design, Integration, and Use of Generative AI in Mental Health.

JMIR mental health
Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is t...

From social media to artificial intelligence: improving research on digital harms in youth.

The Lancet. Child & adolescent health
In this Personal View, we critically evaluate the limitations and underlying challenges of existing research into the negative mental health consequences of internet-mediated technologies on young people. We argue that identifying and proactively add...

Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.

JMIR formative research
BACKGROUND: Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaire...

Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.

JMIR mental health
BACKGROUND: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.

Journal of medical Internet research
BACKGROUND: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment meth...

Key Predictors of Generativity in Adulthood: A Machine Learning Analysis.

The journals of gerontology. Series B, Psychological sciences and social sciences
OBJECTIVES: This study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and me...