Schizophrenia

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT), performed in clinical settings require time and effort. This study aimed to develop a deep learning model using the RCFT and based on eye tracking to...

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their ...

Assessing dimensions of thought disorder with large language models: The tradeoff of accuracy and consistency.

Natural Language Processing (NLP) methods have shown promise for the assessment of formal thought disorder, a hallmark feature of schizophrenia in whi...

Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania.

PURPOSE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in ...

Neural waves and computation in a neural net model II: Data-like structures and the dynamics of episodic memory.

The computational resources of a neuromorphic network model introduced earlier were investigated in the first paper of this series. It was argued that...

Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

OBJECTIVE: Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain co...

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as ...

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction.

This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (...

Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machi...

Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study.

Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes ...

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindi...

Using Human Resources Data to Predict Turnover of Community Mental Health Employees: Prediction and Interpretation of Machine Learning Methods.

This study used machine learning (ML) to predict mental health employees' turnover in the following 12 months using human resources data in a communit...

Deep learning for schizophrenia classification based on natural language processing-A pilot study.

Power spectral density-based resting-state EEG classification of first-episode psychosis.

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The ab...

Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection.

This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychi...

Glycocalyx shedding patterns identifies antipsychotic-naïve patients with first-episode psychosis.

Psychotic disorders have been linked to immune-system abnormalities, increased inflammatory markers, and subtle neuroinflammation. Studies further sug...

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis.

BACKGROUND: Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effecti...

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very stro...

Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.

BACKGROUND: Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life ...

Machine Learning in Electroconvulsive Therapy: A Systematic Review.

Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust w...