Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …

Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have
been largely reported. In the last decades, research has shifted to the identification of …

In recent years, machine learning (ML) has been a promising approach in the research of
treatment outcome prediction in psychosis. In this study, we reviewed ML studies using …

Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …

Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Recent advances in machine learning (ML) promise far-reaching improvements across
medical care, not least within psychiatry. While to date no psychiatric application of ML …

Introduction: Clozapine remains the most effective antipsychotic for treatment-refractory
schizophrenia. However,~ 40% of the patients respond insufficiently to clozapine …

Membrane trafficking processes regulate G protein–coupled receptor (GPCR) activity.
Although class A GPCRs are capable of activating G proteins in a monomeric form, they can …

Magnetic resonance imaging (MRI) has been proposed as a source of information for
automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data …