Psychiatry

From PHQ-2 to PHQ-2W: Data-driven identification of depressed mood and fatigue for optimized weighted depression screening.

OBJECTIVE: This study aims to optimize depression screening tools through a data-driven approach, identifying the most predictive core item combination from the PHQ-9 scale to construct a new simplified depression screening tool. METHODS: Using 11 international datasets, we employed RFECV to select the most predictive item combination from the PHQ-9. Logistic regression models were developed and e...

The effect of spatial and intensity level augmentation of structural magnetic resonance images on autism diagnosis model.

In deep learning, the robustness and generalizability of models significantly depend on diverse and heterogeneous training data. Acquiring such an extensive dataset is challenging in fields like disorder prediction due to data scarcity, which can be attributed to factors such as privacy concerns, limited patient population, or inadequate facilities. Data augmentation can be an ideal solution to th...

The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis.

BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity...

Artificial intelligence in psychiatry: Current and emerging trends, clinical applications, and research gaps explored through a bibliometric analysis.

OBJECTIVE: The aim of this study is to analyze AI-based scientific publications in psychiatry using bibliometric methods to identify prominent themes,...

Acceptance of communication robots among patients undergoing maintenance hemodialysis with psychological stress.

OBJECTIVE: Although several psychotherapeutic interventions have been introduced for patients receiving maintenance hemodialysis (MHD), challenges suc...

NeuMTL: A Unified Multimodal Framework for Multi-Task Prediction in CNS Drug Discovery.

Deep learning has made significant progress in drug discovery. However, most existing models are single-task and single-modality, which not only limit...

Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.

BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes a...

Machine learning detecting aggression among mood disorder patients visited in psychiatric emergency departments.

BACKGROUND: Aggression, which is highly prevalent in patients with mood disorders, has been proven valuable in detecting the progression from hypomani...

Altered resting-state sensorimotor network in patients with obsessive-compulsive disorder: An EEG study.

BACKGROUND AND OBJECTIVE: Dysfunction in the cortical-striatal-thalamo-cortical circuit is considered a core pathological mechanism of obsessive-compu...

Identification of phosphodiesterase 10 A modulators for neurodegenerative and psychiatric disorders: Combination of physics-based virtual screening and machine learning approaches.

Phosphodiesterase (PDE) is a crucial enzyme that regulates intracellular signal transduction by breaking down cyclic adenosine monophosphate (cAMP) an...

Enlarged cavum septum pellucidum & increased incidence of schizophrenia: Narrative review for neurodevelopmental correlate.

Schizophrenia being a major psychiatric disorder comprises of dominant neurodevelopmental corroborations; still there are inadequate markers which rev...

Identifying diagnostic neuroimaging biomarkers for adolescent major depressive disorder.

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide ...

Disrupted brain connectivity in postpartum depression: Insights from resting-state fMRI and machine learning.

BACKGROUND: Postpartum depression (PPD) is a common women's psychological health issue. While studies have identified regional functional abnormalitie...

Humane Intelligence in Geropsychiatric Care: Relational Artificial Intelligence, Clinical Wisdom, and the Moral Grid Operational Index.

Artificial intelligence (AI) already influences how older adults are identified for services, supported between provider visits, and referred for care...

From innovation to implementation: Artificial intelligence in cognitive behaviour therapy training and supervision.

Artificial intelligence (AI) can transform mental health care globally by improving the efficiency, consistency, effectiveness and accessibility of tr...

Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients.

BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains chal...

Optimizing depression diagnosis: fNIRS and machine learning differentiate unipolar, bipolar, and healthy states.

BACKGROUND: Differentiating Unipolar Depression (UD) from Bipolar Depression (BD) remains challenging due to overlapping symptoms, which can lead to m...

Improving systems & communities of psychosis care: Proceedings from the 2024 Mass-STEP Conference.

A 2024 conference titled "Early Intervention in Psychosis: Systems & Communities of Care" brought together researchers, clinicians, policy experts, an...

Biotypes of deeply phenotyped depressed patients reflect signatures of adverse childhood experience and depressive cognitive biases.

BACKGROUND: Negative Cognitive Styles (NCS) are key features of depression contributing to severe clinical outcomes by sustaining negative affect. How...

Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real-time daily data with supervised learning.

The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress...