AIMC Topic: Psychotic Disorders
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Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?
AMA journal of ethics
Sep 1, 2018
Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about mach...
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.
NeuroImage
Jul 25, 2018
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consi...
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP).
Schizophrenia research
May 26, 2018
Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis.
Journal of evaluation in clinical practice
May 23, 2018
RATIONALE: Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate...
Humans
Cytokines
Female
Adult
Psychotic Disorders
Tryptophan
Schizophrenic Psychology
Behavioral Symptoms
Cognitive Reserve
Neurocognitive Disorders
Neuroimmunomodulation
Inflammation
Unsupervised Machine Learning
Patient Acuity
Schizophrenia
Male
Middle Aged
Quality of Life
Psychiatric Status Rating Scales
Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.
Psychiatry research. Neuroimaging
Mar 6, 2018
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...
The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.
Schizophrenia research
Jan 19, 2018
Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire...
Individualized prediction of psychosis in subjects with an at-risk mental state.
Schizophrenia research
Sep 19, 2017
Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and ...
A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.
Schizophrenia research
Aug 23, 2017
Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive, negative and depressive symptoms and reported difficulties differentiating between these symptom do...
BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.
Psychiatry research. Neuroimaging
May 24, 2017
BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It uses a machine learning approach and can be used to analyse deviation from physiological developmenta...
Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.
PloS one
Apr 20, 2017
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipola...
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