AIMC Topic: Psychotic Disorders

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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)...
Female Adult Male Comorbidity Humans Medical Records Systems, Computerized Clinical Coding Database Management Systems Obsessive-Compulsive Disorder Bipolar Disorder Natural Language Processing Schizophrenia Psychotic Disorders
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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...
Models, Statistical Remission Induction Psychotic Disorders Humans Machine Learning Forecasting Quality of Life Treatment Outcome
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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...
Humans Biomarkers Brain Schizophrenia Psychotic Disorders Connectome Middle Aged Bipolar Disorder Support Vector Machine Female Magnetic Resonance Imaging Adult Male
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The risks of risk. Regulating the use of machine learning for psychosis prediction.

International journal of law and psychiatry Aug 17, 2019
Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric assessment. This article has two objectives. First, it clarifies which aspects of English Law are relevant in order to regulate the use ...
Humans Machine Learning United Kingdom Algorithms Psychiatry Risk Assessment Psychotic Disorders
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Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches.

Schizophrenia research May 16, 2019
The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions ...
Monte Carlo Method Ecological Momentary Assessment Emotions Principal Component Analysis Psychotic Disorders Machine Learning Female Male Case-Control Studies ROC Curve Smartphone Logistic Models Support Vector Machine Humans Neural Networks, Computer
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Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

Psychiatry research Mar 29, 2019
This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar dep...
Psychotic Disorders Schizophrenic Psychology Parents Cognition Humans Schizophrenia Machine Learning Female Adult Male Middle Aged Depressive Disorder, Major Bipolar Disorder
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Predicting one-year outcome in first episode psychosis using machine learning.

PloS one Mar 7, 2019
BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier intervention corresponds to significantly better symptomatic and functional outcomes. Our study object...
Adolescent Psychotic Disorders Datasets as Topic Employment Models, Psychological Glasgow Outcome Scale Young Adult Adult Male Psychiatric Status Rating Scales Prognosis Female Educational Status Humans Machine Learning Prospective Studies
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The Early Psychosis Screener for Internet (EPSI)-SR: Predicting 12 month psychotic conversion using machine learning.

Schizophrenia research Feb 15, 2019
INTRODUCTION: A faster and more accurate self-report screener for early psychosis is needed to promote early identification and intervention.
Humans Machine Learning Predictive Value of Tests Risk Assessment Internet Diagnosis, Computer-Assisted Psychotic Disorders Support Vector Machine Early Diagnosis
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Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort.

Progress in neuro-psychopharmacology & biological psychiatry Dec 12, 2018
BACKGROUND: Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia.
Diagnosis, Computer-Assisted Psychiatric Status Rating Scales Cohort Studies Follow-Up Studies Aggression Humans Schizophrenia Adult Machine Learning Psychotic Disorders Sensitivity and Specificity Prognosis Recurrence Male Female Schizophrenic Psychology
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Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.

NeuroImage. Clinical Oct 10, 2018
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not eff...
Magnetic Resonance Imaging Adult Schizophrenia Humans Male Psychotic Disorders Machine Learning Middle Aged Cognitive Behavioral Therapy Female Treatment Outcome
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