Psychiatry

Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population. We utilized machine-learning to identify patient-reported outcome measures (PROMs) that accurately predict major depressive disorder (MDD) and anxiety disorder in an IMID population.

Deep learning in mental health outcome research: a scoping review.

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual's physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients' historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep lear...

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning.

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) h...

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

OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' p...

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studie...

Machine Learning Based Suicide Ideation Prediction for Military Personnel.

Military personnel have greater psychological stress and are at higher suicide attempt risk compared with the general population. High mental stress m...

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

Growing evidence indicates a reciprocal relationship between low-grade systemic inflammation and stress exposure towards increased vulnerability to ne...

Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks.

Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their ow...

Neural memory plasticity for medical anomaly detection.

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including...

Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability.

In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corre...

A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care.

OBJECTIVES: To investigate the effect of a social robot intervention on depression, loneliness, and quality of life of older adults in long-term care ...

Prediction of physical violence in schizophrenia with machine learning algorithms.

Patients with schizophrenia have been shown to have an increased risk for physical violence. While certain features have been identified as risk facto...

Noise can speed backpropagation learning and deep bidirectional pretraining.

We show that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likeliho...

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning ...

Predicting mental health problems in adolescence using machine learning techniques.

BACKGROUND: Predicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing fu...

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning.

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal br...

Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of...

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms ...

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated ...

Brain MRI analysis using a deep learning based evolutionary approach.

Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear unde...