AIMC Topic: Mental Disorders

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Recognizing Image Semantic Information Through Multi-Feature Fusion and SSAE-Based Deep Network.

Journal of medical systems
Images are powerful tools with which to convey human emotions, with different images stimulating diverse emotions. Numerous factors affect the emotions stimulated by the image, and many researchers have previously focused on low-level features such a...

Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach.

Behaviour research and therapy
The availability of large-scale datasets and sophisticated machine learning tools enables developing models that predict treatment outcomes for individual patients. However, few studies used routinely available sociodemographic and clinical data for ...

Analysis of disease comorbidity patterns in a large-scale China population.

BMC medical genomics
BACKGROUND: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

Psychiatry and Fitness to Fly After Germanwings.

The journal of the American Academy of Psychiatry and the Law
In March 2015, a co-pilot flying Germanwings Flight 9525 deliberately pointed his airplane into a descent, killing himself, five other crew members, and 144 passengers. Subsequent investigation and review teams examined the incident and considered po...

Artificial Intelligence for Mental Health and Mental Illnesses: an Overview.

Current psychiatry reports
PURPOSE OF REVIEW: Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original res...

Recommendations and future directions for supervised machine learning in psychiatry.

Translational psychiatry
Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learni...

Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning.

Child abuse & neglect
BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considera...

Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.

BMC medical informatics and decision making
BACKGROUND: Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification o...

The Future of Digital Psychiatry.

Current psychiatry reports
PURPOSE OF REVIEW: Treatments in psychiatry have been rapidly changing over the last century, following the development of psychopharmacology and new research achievements. However, with advances in technology, the practice of psychiatry in the futur...