Schizophrenia

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 ...

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, bra...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years ...

Factors and predictors of length of stay in offenders diagnosed with schizophrenia - a machine-learning-based approach.

BACKGROUND: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clini...

Neural memory plasticity for medical anomaly detection.

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive r...

Prediction of physical violence in schizophrenia with machine learning algorithms.

Patients with schizophrenia have been shown to have an increased risk for physical violence. While c...

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of l...

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from n...

Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study.

BACKGROUND: Many individuals who will experience a first episode of psychosis (FEP) are not detected...

Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography.

BACKGROUND: When many features and a small number of clinical data exist, previous studies have used...

Pragmatics, Theory of Mind and executive functions in schizophrenia: Disentangling the puzzle using machine learning.

OBJECTIVE: Schizophrenia is associated with a severe impairment in the communicative-pragmatic domai...

Diagnosing schizophrenia with network analysis and a machine learning method.

OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been sugges...

Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.

IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients w...

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical develop...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by cons...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain ...

A review on neural network models of schizophrenia and autism spectrum disorder.

This survey presents the most relevant neural network models of autism spectrum disorder and schizop...

Guest editorial: Special issue on machine learning in schizophrenia.