Psychiatry

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a pro...

Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.

Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diver...

Machine Learning Differentiation of Autism Spectrum Sub-Classifications.

PURPOSE: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily livi...

Examining the Impact of Assistive Technology on Psychological Health, Family Education, and Curriculum Research in Japan: Insights from Artificial Intelligence.

This study aims to analyze the effect of psychological health based on artificial intelligence agent technology on the implementation effect of Japane...

Comparison of effectiveness and safety of Da Vinci robot's "3 + 1" and "4 + 1" modes of treatment for colorectal cancer.

To compare the effectiveness of the Da Vinci Surgical Robot System (DSRS) "3 + 1" and "4 + 1" models for colorectal cancer (CRC). A total of 107 patie...

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode an...

Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system.

BACKGROUND: This research project aims to build a Machine Learning algorithm (ML) to predict first-time ADHD diagnosis, given that it is the most freq...

Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.

Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across diff...

Firearm violence exposure and health in 2 national samples of Black and American Indian/Alaska Native adults.

Exposure to firearm violence is widespread and disproportionately experienced by communities of color, with implications for broad health disparities....

Artificial intelligence is set to transform mental health services.

The current development in the field of artificial intelligence and its applications has advantages and disadvantages in the digital age that we now l...

Peripheral blood MicroRNAs as biomarkers of schizophrenia: expectations from a meta-analysis that combines deep learning methods.

OBJECTIVES: This study aimed at identifying reliable differentially expressed miRNAs (DEMs) for schizophrenia in blood meta-analyses combined with de...

Differentiating Between Sexual Offending and Violent Non-sexual Offending in Men With Schizophrenia Spectrum Disorders Using Machine Learning.

Forensic psychiatric populations commonly contain a subset of persons with schizophrenia spectrum disorders (SSD) who have committed sex offenses. A c...

Natural language processing of online support group postings reveals patients' perspectives on strategies for managing psoriasis.

Psoriasis is a chronic skin disorder, and patients encounter high physical and psychosocial burdens. Social media forums feature extensive patient-gen...

The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention...

Automatic identification of schizophrenia employing EEG records analyzed with deep learning algorithms.

Electroencephalography is a method of detecting and analyzing electrical activity in the brain. This electrical activity can be recorded and processed...

Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.

Positive psychology interventions (PPIs) are effective at increasing happiness and decreasing depressive symptoms. PPIs are often administered as self...

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning a...

A robot intervention for adults with ADHD and insomnia-A mixed-method proof-of-concept study.

OBJECTIVE: To investigate individual effects of a three-week sleep robot intervention in adults with ADHD and insomnia, and to explore participants' e...

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depres...

Understanding the impact of digital technology on the well-being of older immigrants and refugees: A scoping review.

BACKGROUND: The fast-paced development of digital technologies in the areas of social media, pet robots, smart homes, and artificial intelligence, amo...