Psychiatry

Presence hallucination induction through robotically mediated somatomotor conflicts: A pooled analysis of 25 experiments.

Hallucinations are significant symptoms in psychiatric and neurodegenerative diseases, that may indicate advanced disease progression or worse disease forms. They are also frequent in healthy individuals, especially elderly or bereaved. Despite their relevance, studying hallucinations in controlled laboratory conditions remains challenging given the limited procedures inducing clinically relevant ...

Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.

BACKGROUND: Post-COVID Fatigue (PCF) is one of the most common issues people face after recovering from COVID-19. Due to the heterogeneity of clinical manifestations and the lack of objective diagnostic criteria, the identification and prediction of PCF remain challenging. This study aimed to use data mining and machine learning techniques for prediction of PCF. METHODS: We analyzed data from 3850...

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

The relationship between artificial intelligence literacy and artificial intelligence anxiety: A cross-sectional study among pediatric nurses.

AIM: This study aimed to investigate the relationship between pediatric nurses' levels of artificial intelligence literacy and artificial intelligence...

Bipolar disorder relapse detection and prediction using smartwatches. A pilot study for machine learning models using anomaly detection methods.

BACKGROUND: Bipolar disorder (BD) is a chronic mental illness with recurrent mood episodes, with up to 70% of patients relapsing within two years. Ear...

Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms.

This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients...

To be Healed or Hacked? - User-Centered Ethical Design for Embodied AI in Mental Health Care.

The global prevalence of mental health disorders has created a substantial treatment gap. To support clinicians and increase access to care, researche...

Objective diagnosis and treatment outcome prediction in major depressive disorder based on a machine learning framework with dried-serum NIRS digital biomarkers.

The impact of the radical urbanization index on depression risk among middle-aged and elderly Chinese: Evidence from a large-scale cross-sectional study.

BACKGROUND: Rapid urbanization in China, while boosting socioeconomic development, has triggered "radical urbanization" characterized by land expansio...

Comparing barriers to employee assistance program utilization in Canada and the United States using natural language processing and machine learning.

Employee Assistance Program (EAP)s provide critical mental health support, yet utilization remains low in both Canada and the United States. Although ...

Using natural language processing to assess MDD psychotherapy patterns among Veterans Affairs patients with suicide risk.

Major depressive disorder (MDD) is a leading risk factor for suicide. Within the US Department of Veterans Affairs (VA), psychotherapy is widely used ...

Second-to-fourth digit (2D:4D) ratio and criminal involvement in schizophrenia.

Speech analysis for differentiating bipolar disorder and major depressive disorder during euthymic states.

Human in the loop: Balancing artificial intelligence, clinical judgment, and legal responsibility.

Artificial intelligence (AI) is increasingly integrated into mental health care, legal decision-making, and forensic practice. Proponents argue that A...

Deciphering risk factors for severe postherpetic neuralgia in patients with herpes zoster: an interpretable machine learning approach.

INTRODUCTION: Postherpetic neuralgia (PHN) is a common complication of herpes zoster (HZ). This study aimed to use a large real-world electronic medic...

Resting-State Functional Connectivity of the Amygdala and Hippocampus in PTSD: Results From the PGC-ENIGMA PTSD Working Group.

OBJECTIVE: Studies investigating resting-state functional connectivity of the amygdala and hippocampus have produced inconsistent findings. The author...

Integrative Dermatology for Longevity: The Synergy of Topical and Internal Approaches.

Skin aging reflects both intrinsic biological decline and extrinsic influences collectively known as the skin exposome, including ultraviolet (UV) rad...

AI Chatbots for Mental Health Self-Management: Lived Experience-Centered Qualitative Study.

BACKGROUND: Large language models (LLMs) now enable chatbots to engage in sensitive mental health conversations, including depression self-management....

Analyzing multiple-sclerosis progression: stage-specific biomarker insights via explainable machine learning.

BACKGROUND: Multiple Sclerosis (MS) is a chronic autoimmune disease where early diagnosis from Clinically Isolated Syndrome (CIS) remains challenging....

Classification of depressed and non-depressed MCI and non-depressed cognitively normal individuals using resting-state metrics: A multi-group study with machine learning and graph reinforcement learning.

Depressive symptoms frequently co-occur in individuals with Mild Cognitive Impairment (MCI) and are thought to accelerate neurodegenerative progressio...