AIMC Topic: Psychotic Disorders

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Brain Fractal Dimension and Machine Learning can predict first-episode psychosis and risk for transition to psychosis.

Computers in biology and medicine
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complex...

Phenomenological psychopathology meets machine learning: A multicentric retrospective study (Mu.St.A.R.D.) targeting the role of Aberrant Salience assessment in psychosis detection.

Schizophrenia research
BACKGROUND: The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Salience Inventory (ASI), a psychometric tool, measures this phenomenon. This study utilized a multi-c...

Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.

Journal of psychiatric research
BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understand communication in psychosis and suicide risk prediction, but not to predict future suicidal behavio...

A Pilot Analysis Investigating the Use of AI in Malingering.

The journal of the American Academy of Psychiatry and the Law
Generative artificial intelligence (AI), with its increasing ubiquity and power, will likely transform forensic psychiatry, sparking both advances and new challenges for the field. A possible consequence of the technology is that it will be used to a...

Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.

Translational psychiatry
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensors which can continuously and unobtrusively measure behaviors (e.g., activity levels, location, and p...

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.

Schizophrenia bulletin
BACKGROUND AND HYPOTHESIS: Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of ...

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

JAMA psychiatry
IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a...

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings ...