Schizophrenia

The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and progression challenging. EEG has emerged as a valuable tool for studying schizophrenia, with machine learning increasingly applied for diagnosis. This paper assesses the accuracy of ML models for predicting schizophrenia and examines the impact of st...

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

SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot." It automates the analysis, retrie...

Feature-Prescribed Iterative Learning Control of Waggle Dance Movement for Social Motor Coordination in Joint Actions

Extensive experiments suggest that motor coordination among human participants may contribute to social affinity and emotional attachment, which has...

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 unclear how they affect schizophrenia susceptibil...

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 important challenges for neuroimaging research. T...

A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in lea...

Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications

The exponential growth in computational power and accessibility has transformed the complexity and scale of bioinformatics research, necessitating s...

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 information from free-text pathology reports across mu...

Anatomical Foundation Models for Brain MRIs

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of th...

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in abou...

Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic facto...

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 for neurodegeneration. We investigated the brain-PA...

Interpreting artificial neural networks to detect genome-wide association signals for complex traits

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environ...

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

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional appr...

HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning

Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-lan...

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, aiding in patient education, diagnostic assistance, t...

MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation

Retrieval-augmented text generation (RAG) addresses the common limitations of large language models (LLMs), such as hallucination, by retrieving inf...

Classification of Schizophrenia using Intrinsic Connectivity Networks and Incremental Boosting Convolution Neural Networks.

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses su...

A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification.

Time courses (TC) and functional network connectivity (FNC) features, derived from functional magnetic resonance imaging, show considerable potential ...

Label Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data.

Neuroimaging data have become widely studied in the context of identifying brain-based markers of mental illness. however, this work is hampered by th...