Psychiatry

Schizophrenia

Latest AI and machine learning research in schizophrenia for healthcare professionals.

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Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to suppor...

The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood.

BACKGROUND: Schizophrenia (SCZ) is a highly heritable mental disorder with a substantial disease bur...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has br...

Moving targets in drug discovery.

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity with...

Comparing machine and deep learning-based algorithms for prediction of clinical improvement in psychosis with functional magnetic resonance imaging.

Previous work using logistic regression suggests that cognitive control-related frontoparietal activ...

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning.

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional...

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics ...

Inner speech.

Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, interna...

Multi-dimensional predictions of psychotic symptoms via machine learning.

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a r...

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symp...

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psyc...

Identifying influential factors distinguishing recidivists among offender patients with a diagnosis of schizophrenia via machine learning algorithms.

PURPOSE: There is a lack of research on predictors of criminal recidivism of offender patients diagn...

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this st...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G pro...

A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI.

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for di...

Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with t...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not ...

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed i...

Language as a biomarker for psychosis: A natural language processing approach.

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical ...

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