AI Medical Compendium Topic

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Psychiatry

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Mapping the Delirium Literature Through Probabilistic Topic Modeling and Network Analysis: A Computational Scoping Review.

Psychosomatics
BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultat...

Translational machine learning for psychiatric neuroimaging.

Progress in neuro-psychopharmacology & biological psychiatry
Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the prediction of diagnoses, prognoses, and optimal therapeutic strategies. Machine learning approaches may enhance the translational poten...

Annual Research Review: Developmental computational psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disord...

Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning.

Biological psychiatry. Cognitive neuroscience and neuroimaging
Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the future, as opposed to making a diagnosis, which is concerned with the current state. During the follow-up period, many factors will influence the course of...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large...

Machine Learning Approaches for Clinical Psychology and Psychiatry.

Annual review of clinical psychology
Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an access...

Machine Learning for Precision Psychiatry: Opportunities and Challenges.

Biological psychiatry. Cognitive neuroscience and neuroimaging
The nature of mental illness remains a conundrum. Traditional disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet psychiatrists and investigators now have an unprecedented opportunity to benefit...

Teaching to See Behaviors-Using Machine Learning?

Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry.

Journal of psychiatric research
Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted f...