AIMC Topic: Mental Disorders

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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...

Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm.

AMA journal of ethics
A significant proportion of elderly and psychiatric patients do not have the capacity to make health care decisions. We suggest that machine learning technologies could be harnessed to integrate data mined from electronic health records (EHRs) and so...

Predeployment predictors of psychiatric disorder-symptoms and interpersonal violence during combat deployment.

Depression and anxiety
BACKGROUND: Preventing suicides, mental disorders, and noncombat-related interpersonal violence during deployment are priorities of the US Army. We used predeployment survey and administrative data to develop actuarial models to identify soldiers at ...

Drug Repositioning for Schizophrenia and Depression/Anxiety Disorders: A Machine Learning Approach Leveraging Expression Data.

IEEE journal of biomedical and health informatics
Development of new medications is a lengthy and costly process, and drug repositioning might help to shorten the development cycle. We present a machine learning (ML) workflow to drug discovery or repositioning by predicting indication for a particul...

Active Inference in OpenAI Gym: A Paradigm for Computational Investigations Into Psychiatric Illness.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Artificial intelligence has recently attained humanlike performance in a number of gamelike domains. These advances have been spurred by brain-inspired architectures and algorithms such as hierarchical filtering and reinforcement learning...

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...

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...

Automatic mining of symptom severity from psychiatric evaluation notes.

International journal of methods in psychiatric research
OBJECTIVES: As electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making...

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...