AIMC Topic: Schizophrenia

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Sch-net: a deep learning architecture for automatic detection of schizophrenia.

Biomedical engineering online
BACKGROUND: Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. Clinically, schizophrenia with negative symptoms is usually misdiagnosed. The diagnosis is also dependent on the experienc...

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Human brain mapping
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level...

Analysis of cognitive impairment in schizophrenia based on machine learning: Interaction between psychological stress and immune system.

Neuroscience letters
The interaction between psychological stress and immune system may be associated with the cognitive impairment of schizophrenia. To employ machine learning algorithms to examine patterns of stress-immune networks with cognitive impairment in chronic ...

Going deep into schizophrenia with artificial intelligence.

Schizophrenia research
Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the r...

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

International journal of environmental research and public health
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software w...

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions.

Psychiatry research. Neuroimaging
Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture meas...

EEG microstate features for schizophrenia classification.

PloS one
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes ...

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.

Scientific reports
Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia' functional outcomes such as Quality of Life...

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment.

Psychological medicine
The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that 'vitals' are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxyg...

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...