Psychiatry

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a greater exposure to general risk factors for poor cognition.

Extracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model.

OBJECTIVES: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of ...

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessme...

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizi...

Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.

Accurate prediction of suicide risk is crucial for identifying patients with elevated risk burden, helping ensure these patients receive targeted care...

Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior....

Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.

PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This likely contributes to the limited success of exist...

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatmen...

Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to devel...

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. Fro...

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution Learning.

Machine learning methodologies have been profoundly researched in the realm of autism spectrum disorder (ASD) diagnosis. Nonetheless, owing to the amb...

mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.

In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on ...

AI-driven report-generation tools in mental healthcare: A review of commercial tools.

Artificial intelligence (AI) systems are increasingly being integrated in clinical care, including for AI-powered note-writing. We aimed to develop an...

Artificial Intelligence (AI) and academic publishing in psychiatry.

The current and potential impact of various applications of artificial intelligence (AI) to the field of academic publishing in psychiatry is the subj...

A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnos...

Optimizing Speech-Input Length for Speaker-Independent Depression Classification

Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression cl...

STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis

Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and att...

GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study

Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can ...

A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, men...

Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models f...