Schizophrenia

Multi-center brain age prediction via dual-modality fusion convolutional network.

Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be address...

Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls.

Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potential misrepresentations and consequent inequalities related to people with mental health disorders. Hence occupational and mental health bias in existing Natural Language Processing (NLP) models used in recruiting and job hunting must be assessed. We e...

MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.

The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these s...

Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol.

The application of personalized medicine in patients with first-episode psychosis (FEP) requires tools for classifying patients according to their res...

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parc...

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain ...

On the improvement of schizophrenia detection with optical coherence tomography data using deep neural networks and aggregation functions.

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many effor...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique...

How can language models assist with pharmaceuticals manufacturing deviations and investigations?

Large Language Models (LLM) such as the Generative-Pretrained-Transformer (GPT) and Large-Language-Model-Meta-AI (LLaMA) have attracted much attention...

Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.

In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comment...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Uncovering key predictive channels and clinical variables in the gamma band auditory steady-state response in early-stage psychosis: a longitudinal study.

OBJECTIVE: Psychotic disorders are characterised by abnormalities in the synchronisation of neuronal responses. A 40 Hz gamma band deficit during audi...

Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis.

Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligen...

AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.

OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted...

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and ...

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, ...

Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations.

Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. Howeve...

Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.

OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients wi...

Evolution of Linguistic Markers of Agency, Centrality and Content During Metacognitive Therapy for Psychosis: A Pilot Exploratory Study.

AIM: Metacognitive Reflection and Insight Therapy (MERIT) is a form of person-centred psychotherapy that promotes recovery-oriented outcomes by target...

The Role of Artificial Intelligence in Obesity Medicine.

The rising prevalence of obesity presents significant health, economic, and social challenges, necessitating a comprehensive approach to prevention, d...