AIMC Topic: Adult

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Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

The Journal of clinical psychiatry
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...

Machine Learning Based Suicide Ideation Prediction for Military Personnel.

IEEE journal of biomedical and health informatics
Military personnel have greater psychological stress and are at higher suicide attempt risk compared with the general population. High mental stress may cause suicide ideations which are crucially driving suicide attempts. However, traditional statis...

A double-hit of stress and low-grade inflammation on functional brain network mediates posttraumatic stress symptoms.

Nature communications
Growing evidence indicates a reciprocal relationship between low-grade systemic inflammation and stress exposure towards increased vulnerability to neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, the neural correl...

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Biointerphases
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and e...

Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.

International journal of medical informatics
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.

Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

Annals of clinical and translational neurology
OBJECTIVE: Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterog...

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.

European radiology experimental
BACKGROUND: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence...

Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment.

Scientific reports
Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback ...