Latest AI and machine learning research in psychiatry for healthcare professionals.
In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to stream...
To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the largest benefit can be considered. Recently, a machine learning approach, O-learning, has been proposed to estimate an optimal individualized treatment rule (ITR), but it is developed to make binary decisions and thus limited to compare two treatment...
BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering cl...
Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing ...
This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression t...
BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-bas...
The cognitive and behavioral interventions can be as efficacious as antidepressant medications and more enduring, but some patients will be more likel...
BACKGROUND: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be cons...
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in t...
Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or ea...
BACKGROUND: Previous automated EEG-based diagnosis of autism spectrum disorders (ASD) using various nonlinear EEG analysis methods were limited to dis...
Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel ap...
Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarker...
Nociceptin/Orphanin FQ (N/OFQ) is a neuropeptide that modulates pain transmission, learning/memory, stress, anxiety, and fear responses via activation...
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial...
For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancin...
From artificial intelligence, predictive analytics and biometric sensors, to advanced robotics, virtual reality and mobile applications, rapid advance...
Fear of pain demonstrates significant prognostic value regarding the development of persistent musculoskeletal pain and disability. Its assessment oft...
Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms h...
This paper describes the INSIDE system, a networked robot system designed to allow the use of mobile robots as active players in the therapy of childr...