AIMC Topic: Mental Disorders

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Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

Experimental neurology
By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into method...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Nature neuroscience
Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesized to have relevance to brain development and neuropsychiatric disease. However, the detection of individual somatic mobile element insertions present...

Predicting hospitalization following psychiatric crisis care using machine learning.

BMC medical informatics and decision making
BACKGROUND: Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this pa...

How wide is the application of genetic big data in biomedicine.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore ...

Smart conversational agents for the detection of neuropsychiatric disorders: A systematic review.

Journal of biomedical informatics
OBJECTIVE: To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field...

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Psychiatry research
Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading this path, mental health practice has gradually moved to Digital Mental Health Interventions (DMHI), t...

Machine learning for psychiatry: getting doctors at the black box?

Molecular psychiatry
Recent developments in the field of machine learning have spurred high hopes for diagnostic support for psychiatric patients based on brain MRI. But while technical advances are undoubtedly remarkable, the current trajectory of mostly proof-of-concep...

A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex.

Proceedings of the National Academy of Sciences of the United States of America
The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underl...

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics.

Personalized medicine
There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized ...