Latest AI and machine learning research in psychiatry for healthcare professionals.
Diazepam is one of the most widely prescribed tranquilizers for the therapy of alcohol withdrawal syndrome (AWS), which includes the symptoms of anxiety, fear, and emotional tension. However, diazepam therapy often turns out to be ineffective, and some patients experience dose-dependent adverse drug reactions, reducing the efficacy of therapy. The purpose of our study was to investigate the effe...
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies al...
IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant m...
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors...
Pediatric acute onset neuropsychiatric syndrome (PANS) is a clinically heterogeneous disorder presenting with: unusually abrupt onset of obsessive co...
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong d...
OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocia...
Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing...
To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive sympto...
Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, th...
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, an...
The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD interven...
At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to pre...
Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, econo...
OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal c...
This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work compose...
Artificial intelligence has dramatically changed the world as we know it, but is yet to fully embrace 'hot' cognition, i.e., the way an intelligent be...
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...
BACKGROUND: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of fact...
Managing the balance between accurately identifying early stage melanomas while avoiding obtaining biopsy specimens of benign lesions (ie, overbiopsy)...