Psychiatry

A Randomized Study Using Telepresence Robots for Behavioral Health in Interprofessional Practice and Education.

The events of the coronavirus disease 2019 (COVID-19) pandemic forced the world to adopt telemedicine frameworks to comply with isolation and stay-at-home regulations. Telemedicine, in various forms, has been used by patients and medical professionals for quite some time, especially telepsychiatry. To examine the efficacy and role of telesimulation as a method to educate health sciences students ...

Surgical technique for mesorectal division during robot-assisted laparoscopic tumor-specific mesorectal excision (TSME) for rectal cancer using da Vinci Si surgical system: the simple switching technique (SST).

In a narrow pelvic cavity, performing sufficient tumor-specific mesorectal excision (TSME) is difficult. Even in robot-assisted laparoscopic surgery (RALS), mesorectal division is difficult in a narrow pelvic cavity. To overcome this difficulty, we invented a novel method of mesorectal division. In this new approach, we switched the fenestrated bipolar forceps and the double-fenestrated forceps wi...

Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes.

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of th...

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Althoug...

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.

BACKGROUND: The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were diffe...

A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach.

BACKGROUND: Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD) are considered leading causes of life-long disability worldwide,...

Gut microbiome-mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis.

The gut-brain axis (GBA) is a biochemical link that connects the central nervous system (CNS) and enteric nervous system (ENS). Clinical and experimen...

BrainNET: Inference of Brain Network Topology Using Machine Learning.

To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm...

Deep neural networks detect suicide risk from textual facebook posts.

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chanc...

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms.

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health meas...

Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning.

Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as ...

Can machine learning be useful as a screening tool for depression in primary care?

Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mentio...

[Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?].

Currently, we are witnessing an increasing interest in predictive models and personalized diagnosis and treatment choice in psychiatric research. Agai...

The study of the differences between low-functioning autistic children and typically developing children in the processing of the own-race and other-race faces by the machine learning approach.

OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavio...

Inner speech.

Inner speech travels under many aliases: the inner voice, verbal thought, thinking in words, internal verbalization, "talking in your head," the "litt...

Optimal robot for intervention for individuals with autism spectrum disorders.

With recent rapid advances in technology, human-like robots have begun functioning in a variety of ways. As increasing anecdotal evidence suggests, ro...

Multi-dimensional predictions of psychotic symptoms via machine learning.

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of ...

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vac...

A scoping review of machine learning in psychotherapy research.

Machine learning (ML) offers robust statistical and probabilistic techniques that can help to make sense of large amounts of data. This scoping review...

Precision psychiatry in clinical practice.

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance...