Psychiatry

Schizophrenia

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

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The Association Between Psychotropic Medications and Cognitive Functioning in a Real-World Cohort of 869 Individuals with Schizophrenia.

BACKGROUND: Cognitive deficits are a central feature of schizophrenia for which there are not any es...

Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.

BACKGROUND AND OBJECTIVE: Accurate detection of schizophrenia poses a grand challenge as a complex a...

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which di...

Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.

IMPORTANCE: The diagnosis of schizophrenia and bipolar disorder is often delayed several years despi...

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) featur...

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

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial...

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

PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This ...

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, a...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagn...

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering...

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data.

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is...

Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

BACKGROUND: Schizophrenia is a complex and disabling mental disorder that represents one of the most...

Enhancing Clinical Data Extraction from Pathology Reports: A Comparative Analysis of Large Language Models.

This study evaluates the efficacy of a small large language model (sLLM) in extracting critical info...

Deep Learning-based Brain Age Prediction in Patients With Schizophrenia Spectrum Disorders.

BACKGROUND AND HYPOTHESIS: The brain-predicted age difference (brain-PAD) may serve as a biomarker f...

Applications of Artificial Intelligence in Psychiatric Nursing: A Scope Review.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursi...

Transforming nursing with large language models: from concept to practice.

Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aid...

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As s...

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