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 the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. ...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

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 constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing ...

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 in several psychiatric conditions, but is a general...

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

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onse...

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, brain structural, and clinical/demographic predictors...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...

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 clinical concerns. Due to the confounded nature of fact...

Neural memory plasticity for medical anomaly detection.

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including...

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 certain features have been identified as risk facto...

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 large scale brain networks. Using machine learning ...

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

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal br...

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 before occurrence, limiting the effect of prevent...

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 a few top-ranked features from the Fisher's discr...

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 domain. Recent research has tried to disentangle the re...

Diagnosing schizophrenia with network analysis and a machine learning method.

OBJECTIVE: Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the...

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 with first-episode schizophrenia.

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 development. Although neural maturation in humans can be ...

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 considering all CAARMS' symptoms at first presentation...

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 abnormalities. In the past few years, there has be...

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 schizophrenia, from the first connectionist models to rec...