AIMC Topic: Psychotic Disorders

Clear Filters Showing 41 to 50 of 103 articles

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

Schizophrenia research Jun 1, 2020
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarke...
Biomarkers Language Natural Language Processing Psychotic Disorders Humans Prospective Studies
View on PubMed DOI

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

The Lancet. Digital health Mar 26, 2020
BACKGROUND: Many individuals who will experience a first episode of psychosis (FEP) are not detected before occurrence, limiting the effect of preventive interventions. The combination of machine-learning methods and electronic health records (EHRs) ...
Data Management Area Under Curve Models, Biological Prognosis Case-Control Studies Psychotic Disorders Schizophrenia Retrospective Studies Cohort Studies Datasets as Topic Adult Machine Learning Electronic Health Records Humans ROC Curve Male Data Analysis Neural Networks, Computer Middle Aged Delivery of Health Care Female Risk Assessment Databases, Factual
View on PubMed DOI

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

European child & adolescent psychiatry Dec 23, 2019
To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation and using a multivariate machine learning method known as logistic regression with Elastic-net shri...
Prodromal Symptoms Female Risk Factors Young Adult Male Psychiatric Status Rating Scales Adolescent Psychotic Disorders Multivariate Analysis Humans Machine Learning
View on PubMed DOI

Decoding rumination: A machine learning approach to a transdiagnostic sample of outpatients with anxiety, mood and psychotic disorders.

Journal of psychiatric research Dec 7, 2019
OBJECTIVE: To employ machine learning algorithms to examine patterns of rumination from RDoC perspective and to determine which variables predict high levels of maladaptive rumination across a transdiagnostic sample.
Female Adult Male Middle Aged Humans Cytokines Psychotic Disorders Mood Disorders Social Class Adaptation, Psychological Anxiety Disorders Severity of Illness Index Models, Theoretical Support Vector Machine Rumination, Cognitive
View on PubMed DOI

Guest editorial: Special issue on machine learning in schizophrenia.

Schizophrenia research Nov 9, 2019
Psychotic Disorders Neuroimaging Humans Machine Learning Schizophrenia
View on PubMed DOI

Analysis of risk factor domains in psychosis patient health records.

Journal of biomedical semantics Oct 31, 2019
BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important b...
Psychotic Disorders Data Mining Biological Ontologies Risk Factors Humans Young Adult Neural Networks, Computer Male Electronic Health Records Natural Language Processing Female
View on PubMed DOI

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Translational psychiatry Oct 17, 2019
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predicto...
Young Adult Adult Male Middle Aged Risk Assessment Diagnosis, Computer-Assisted Adolescent Registries Humans Machine Learning Prognosis Female Psychotic Disorders London
View on PubMed DOI

Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder.

Scientific reports Oct 2, 2019
Obsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP)...
Bipolar Disorder Female Natural Language Processing Medical Records Systems, Computerized Adult Schizophrenia Humans Clinical Coding Male Psychotic Disorders Database Management Systems Comorbidity Obsessive-Compulsive Disorder
View on PubMed DOI

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

The Lancet. Digital health Sep 12, 2019
BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multiva...
Treatment Outcome Machine Learning Humans Models, Statistical Forecasting Remission Induction Quality of Life Psychotic Disorders
View on PubMed DOI

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

Schizophrenia research Aug 19, 2019
BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReH...
Bipolar Disorder Schizophrenia Female Support Vector Machine Psychotic Disorders Humans Adult Magnetic Resonance Imaging Connectome Biomarkers Male Brain Middle Aged
View on PubMed DOI
Popular Topics
Recent Journals