Psychiatry

Latest AI and machine learning research in psychiatry for healthcare professionals.

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Comparative Medical Ecology of Gut Microbiomes in Major Neurodegenerative, Neurodevelopmental, and Psychiatric (NNP) Disorders

This study provides a comprehensive medical ecology analysis of gut microbiome alterations in four neuropsychiatric disorders: Alzheimer’s disease (AD), Parkinson’s disease (PD), autism spectrum disorder (ASD), and the mood disorders (MDs) including major depressive disorder (MDD) (depression) and bipolar disorder (BD). Using diversity, heterogeneity, specificity, and AI/machine learning approache...

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models

Clinical phenotyping is the process of extracting patient’s observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-injury, to identify suicide risks and improve interventions. In this study, we leveraged the latest reasoning models, namely 4o, o1, and o3-m...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexp...

Paving the way for precision treatment of psychiatric symptoms with functional connectivity neurofeedback

Major depressive disorder (MDD) remains challenging to treat, with many patients failing to respond adequately to existing therapies. Patients with MD...

Predicting Suicidality in people living with HIV in Uganda: A Machine Learning Approach

People living with HIV (PLWH) are more likely to experience suicidal thoughts and exhibit suicidal behavior than the general population. However, ther...

Blood Immuno-metabolic Biomarker Signatures of Depression and Affective Symptoms in Young Adults

Depression is associated with alterations in immuno-metabolic biomarkers, but it remains unclear whether these alterations are limited to specific mar...

PREACT-digital: Study protocol for a longitudinal, observational multi-center study on wearable- and EMA- based predictors of non-response to CBT for internalizing disorders

Despite CBT’s status as a first-line treatment, a substantial proportion of patients does not experience sufficient symptom relief. Recent advances in...

Machine Learning in Psychiatric Health Records: A Gold Standard Approach to Trauma Annotation

Psychiatric electronic health records present unique challenges for machine learning due to their unstructured, complex, and variable nature. This stu...

Identifying Predictors of Benzodiazepine Discontinuation in Medical Cannabis Patients with Post-traumatic Stress Disorder Using a Machine Learning Approach

Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition commonly treated with medications like benzodiazepines (BZDs), despite...

Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals

Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomi...

LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases

Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their ...

Perceptions and Insights: A Qualitative Assessment of an AI-Assisted Psychiatric Triage System Implemented in an Outpatient Hospital Setting

The Canadian healthcare system is approaching a breaking point. With mental health being a leading cause of disability, innovative solutions are neces...

Encoding of pretrained large language models mirrors the genetic architectures of human psychological traits

Recent advances in large language models (LLMs) have prompted a frenzy in utilizing them as universal translators for biomedical terms. However, the b...

Finding the Forest in the Trees: Using Machine Learning and Online Cognitive and Perceptual Measures to Predict Adult Autism Diagnosis

Traditional subjective measures are limited in the insight they provide into underlying behavioral differences associated with autism and, accordingly...

Supervised Machine-Learning Classification of Treatment-Resistant Depression in U.S. Claims Data

Accurate identification of individuals with treatment-resistant depression (TRD) is important to facilitate timely access to appropriate care. However...

A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection

Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes...

Artificial Intelligence for Contextual Well-being: Protocol for an Exploratory Sequential Mixed Methods Study with Medical Students as a Social Microcosm

AI-powered conversational agents have proven effective in alleviating psychological distress, however, concerns about autonomy and authentic psycholog...

A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis

Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain ga...

Exploring the Potential of Large Language Models for Automated Safety Plan Scoring in Outpatient Mental Health Settings

The Safety Planning Intervention (SPI) produces a plan to help manage patients’ suicide risk. High-quality safety plans – that is, those with greater ...

Polygenic Risk-Informed White Matter Integrity Improves Deep Learning-Based Prediction of Youth Depression

Early detection of youth depression is crucial, given its rising prevalence and long-term consequences. Although genetic factors contribute significan...

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