AIMC Topic: Psychotic Disorders

Clear Filters Showing 41 to 50 of 109 articles

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Scientific reports Jan 12, 2021
Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset,...
Mental Health Services Humans Natural Language Processing Retrospective Studies Psychotic Disorders Electronic Health Records Symptom Assessment Mental Health
View on PubMed DOI

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

PloS one Dec 31, 2020
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...
Self-Injurious Behavior Mental Health Services Area Under Curve Psychotic Disorders Antipsychotic Agents Suicidal Ideation Bipolar Disorder Child ROC Curve Humans Machine Learning Female Young Adult Adult Male Adolescent
View on PubMed DOI

Multi-dimensional predictions of psychotic symptoms via machine learning.

Human brain mapping Sep 1, 2020
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have pr...
Motivation Prognosis Anhedonia Female Young Adult Hallucinations Humans Adult Magnetic Resonance Imaging Auditory Perception Machine Learning Male Schizophrenia Middle Aged Psychotic Disorders Functional Neuroimaging Attention Adolescent
View on PubMed DOI

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...
Natural Language Processing Psychotic Disorders Humans Prospective Studies Biomarkers Language
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) ...
Male Middle Aged Risk Assessment Case-Control Studies Retrospective Studies Electronic Health Records Female Adult Models, Biological Schizophrenia Psychotic Disorders Datasets as Topic Cohort Studies ROC Curve Data Management Area Under Curve Humans Neural Networks, Computer Machine Learning Data Analysis Delivery of Health Care Databases, Factual Prognosis
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...
Multivariate Analysis Prodromal Symptoms Male Psychiatric Status Rating Scales Adolescent Psychotic Disorders Machine Learning Female Risk Factors Young Adult Humans
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.
Humans Cytokines Female Rumination, Cognitive Adult Psychotic Disorders Male Anxiety Disorders Mood Disorders Middle Aged Severity of Illness Index Social Class Models, Theoretical Adaptation, Psychological Support Vector Machine
View on PubMed DOI

Guest editorial: Special issue on machine learning in schizophrenia.

Schizophrenia research Nov 9, 2019
Schizophrenia Psychotic Disorders Neuroimaging Humans Machine Learning
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...
Natural Language Processing Psychotic Disorders Data Mining Biological Ontologies Humans Female Neural Networks, Computer Risk Factors Electronic Health Records Young Adult Male
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...
Risk Assessment Diagnosis, Computer-Assisted Adolescent Humans Registries Machine Learning Young Adult Prognosis Adult Female Male Psychotic Disorders Middle Aged London
View on PubMed DOI
Popular Topics
Recent Journals