Psychiatry

Machine learning approach for early detection of autism by combining questionnaire and home video screening.

BACKGROUND: Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool.

[Artificial intelligence in psychiatry-an overview].

Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely human. In this overview, the underlying methodological developments that made this possible are briefly delineated and then the applications to psych...

Predictive markers of depression in hypertension.

Hypertension and depression, as 2 major public health issues, are closely related. For patients having hypertension, in particular, depression is a ri...

Factors associated with viral suppression among HIV-positive Kenyan gay and bisexual men who have sex with men.

The UNAIDS 90-90-90 target has prioritized achieving high rates of viral suppression. We identified factors associated with viral suppression among HI...

Measuring Use of Evidence Based Psychotherapy for Posttraumatic Stress Disorder in a Large National Healthcare System.

To derive a method of identifying use of evidence-based psychotherapy (EBP) for post-traumatic stress disorder (PTSD), we used clinical note text from...

Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM.

Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. ...

Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier.

Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disabilit...

Depression Severity Classification from Speech Emotion.

Major Depressive Disorder (MDD) is a common psychiatric illness. Automatically classifying depression severity using audio analysis can help clinical ...

A Sibling-Mediated Intervention for Children with Autism Spectrum Disorder: Using the Natural Language Paradigm (NLP).

We taught three typically developing siblings to occasion speech by implementing the Natural Language Paradigm (NLP) with their brothers with autism s...

Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth.

This study explored the use of unsupervised machine learning to identify subgroups of patients with heart failure who used telehealth services in the ...

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI.

Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic su...

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.

The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers ...

Impressions of Humanness for Android Robot may Represent an Endophenotype for Autism Spectrum Disorders.

Identification of meaningful endophenotypes may be critical to unraveling the etiology and pathophysiology of autism spectrum disorders (ASD). We inve...

AI opens new frontier for suicide prevention.

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

OBJECTIVE: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large se...

A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis.

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machi...

Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.

The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subje...

Automatic Detection of Depression by Using a Neural Network.

Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and he...

Factors Associated with HIV Viral Suppression Among Transgender Women in Lima, Peru.

PURPOSE: Globally, transgender women (TGW) experience a high burden of adverse health outcomes, including a high prevalence of HIV and sexually transm...

A New Local Bipolar Autoassociative Memory Based on External Inputs of Discrete Recurrent Neural Networks With Time Delay.

In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation ...