Psychiatry

Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.

BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain management protocols often fail to accommodate this variability, necessitating more tailored approaches.

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Introducción: la ideación suicida es el pensamiento de autoeliminación no siempre reportada por los pacientes en test de depresión. El objetivo fue identificar y analizar síntomas depresivos del cuestionario de salud del paciente-9 asociados a ideación suicida en la población peruana. Material y métodos: estudio observacional, analítico y transversal basado en datos de 32.062 participantes de la e...

Evaluating the agreement between ChatGPT-4 and validated questionnaires in screening for anxiety and depression in college students: a cross-sectional study.

BACKGROUND: The Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence-based web application, has demonstrated substantial pote...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus ...

The promise and challenges of computer mouse trajectories in DMHIs - A feasibility study on pre-treatment dropout predictions.

With the impetus of Digital Mental Health Interventions (DMHIs), complex data can be leveraged to improve and personalize mental health care. However,...

Enhancing student-centered walking environments on university campuses through street view imagery and machine learning.

Campus walking environments significantly influence college students' daily lives and shape their subjective perceptions. However, previous studies ha...

Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated...

Leveraging artificial intelligence for diagnosis of children autism through facial expressions.

The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder ...

CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.

Human emotions are not necessarily tends to produce right facial expressions as there is no well defined connection between them. Although, human emot...

Developing a standardized framework for evaluating health apps using natural language processing.

Despite regulatory efforts, many smartphone health applications remain unregulated, raising concerns about privacy, security, and evidence-based effec...

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying...

Prediction of first attempt of suicide in early adolescence using machine learning.

BACKGROUND: Suicide is the second leading cause of death among early adolescents, yet the first onset of suicide attempts during this critical develop...

Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery.

BACKGROUND: Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-base...

Direct-acting antivirals (DAA) positively affect depression and cognitive function in patients with chronic hepatitis C.

The aim of the study was to determine how depression and cognitive dysfunction in patients with chronic hepatitis C virus (HCV) infection are affected...

AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity.

OBJECTIVE: Although patients prefer describing their problems using words, mental health interventions are commonly evaluated using rating scales. For...

Estimating depression severity in narrative clinical notes using large language models.

BACKGROUND: Depression treatment guidelines emphasize measurement-based care using patient-reported outcome measures, yet their impact on narrative do...

Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications.

Prolonged exposure to high-stress environments can lead to mental illnesses such as anxiety disorders, depression, and posttraumatic stress disorder. ...

Effect of a ChatGPT-based digital counseling intervention on anxiety and depression in patients with cancer: A prospective, randomized trial.

BACKGROUND: Psychological distress is prevalent among newly diagnosed cancer patients, often exacerbating treatment-related anxiety and depression. Ar...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental h...