Schizophrenia

Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.

Brain alterations associated with illness severity in schizophrenia remain poorly understood. Establishing linkages between imaging biomarkers and symptom expression may enhance mechanistic understanding of acute psychotic illness. Constructing models using MRI and clinical features together to maximize model validity may be particularly useful for these purposes. A multi-task deep learning model ...

Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.

Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functional magnetic resonance imaging (fMRI), genes, and the gut microbiome. Previous studies reported combining multimodal data can offer complementary information for better depicting the abnormalities of SCZ. However, the existing multimodal-based method...

An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial ...

Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.

BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimic...

Subcortical and insula functional connectivity aberrations and clinical implications in first-episode schizophrenia.

INTRODUCTION: Schizophrenia is a complex mental disorder whose pathophysiology remains elusive, particularly in the roles of subcortex. This study aim...

Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.

This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndrom...

Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.

With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages o...

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric con...

Automated linguistic analysis in youth at clinical high risk for psychosis.

Identifying individuals at clinical high risk for psychosis (CHRP) is crucial for preventing psychosis and improving the prognosis for schizophrenia. ...

Cognitive profiles across the psychosis continuum.

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial varia...

Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms.

BACKGROUND AND OBJECTIVE: The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling ti...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this s...

Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.

Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state funct...

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

INTRODUCTION: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated ...

Forecasting the incidence frequencies of schizophrenia using deep learning.

Mental disorders are becoming increasingly prevalent worldwide, and accurate incidence forecasting is crucial for effective mental health strategies. ...

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggeste...

Optimizing biomedical information retrieval with a keyword frequency-driven prompt enhancement strategy.

BACKGROUND: Mining the vast pool of biomedical literature to extract accurate responses and relevant references is challenging due to the domain's int...

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Lar...

Immune-based Machine learning Prediction of Diagnosis and Illness State in Schizophrenia and Bipolar Disorder.

BACKGROUND: Schizophrenia and bipolar disorder frequently face significant delay in diagnosis, leading to being missed or misdiagnosed in early stages...

Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education.

Generative artificial intelligence and large language models are the continuation of a technological revolution in information processing that began w...