AIMC Topic: Psychopathology
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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females.
Scientific reports
Jan 3, 2025
Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30-60% of the risk for onset. While previous studies have separately associated reductions in gray matter volume (G...
Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.
Development and psychopathology
Aug 22, 2023
Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study ...
The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network.
Scientific reports
Dec 16, 2022
The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Ph...
Evaluation of the correlation between gaze avoidance and schizophrenia psychopathology with deep learning-based emotional recognition.
Asian journal of psychiatry
Dec 28, 2021
OBJECTIVE: To investigate the correlation between gaze avoidance and psychopathology in patients with schizophrenia through eye movement measurements in real-life interpersonal situations.
[Digitalized psychiatry : Critical considerations on a new paradigm].
Der Nervenarzt
Sep 13, 2021
Digitalization and artificial intelligence hold the prospect of new procedures for psychiatry. Machine learning techniques combined with big data should enable algorithmized diagnostics, prediction and therapy that are superior to clinical observatio...
Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.
IEEE journal of biomedical and health informatics
Apr 26, 2019
Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This p...
Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.
PloS one
Jan 16, 2019
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause c...
Methodological Advances in the Study of Hidden Variables: A Demonstration on Clinical Alcohol Use Disorder Data.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Research in the domain of psychopathology has been hindered by hidden variables-variables that are important to understanding and treating psychopathological illnesses but are unmeasured. Recent methodological advances in machine learning have culmin...
Estimating psychopathological networks: Be careful what you wish for.
PloS one
Jun 23, 2017
Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These ...
A symptom network structure of the psychosis spectrum.
Schizophrenia research
Feb 22, 2017
Current diagnostic systems mainly focus on symptoms needed to classify patients with a specific mental disorder and do not take into account the variation in co-occurring symptoms and the interaction between the symptoms themselves. The innovative ne...
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