Psychiatry

The effect of psychotherapy on the multivariate association between insomnia and depressive symptoms in late-life depression

Late-life depression (LLD) is prevalent in older adults and linked to increased disability, mortality, and suicide risk. Insomnia symptoms are considered common remaining symptoms of LLD following treatment. However, the multivariate relationship between insomnia and depressive symptoms and the impact of psychotherapy on their interrelationship is insufficiently assessed. We used data from 185 pat...

Predicting Positive Psychological States using Machine Learning and Digital Biomarkers from Everyday Wearable Data

Wearable devices offer continuous physiological data collection, presenting new opportunities for real-world mental health monitoring. Previous research has primarily emphasized detecting stress and psychological states associated with mental illnesses, whereas predicting positive psychological states, such as self-esteem, positive affect, and meaning in life, remains underexplored. In this study,...

Leveraging Large Language Models for Digital Phenotyping: Detecting Depressive State Changes for Patients with Depressive Episodes

Digital phenotyping, which takes advantage of data continuously gathered from smartphones and wearable devices, offers promising avenues for real-time...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...

The allostatic overload in pregnancy during the COVID-19 pandemic and potential effects on the health of the mother-child dyad: Study Protocol

Allostatic load refers to the cumulative burden of stress and life events that involve the interaction of various physiological systems at differing l...

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approach

Maternal mental health (MMH) disorders, particularly depression and anxiety, are major public health concerns in low- and middle-income countries (LMI...

Biomarkers of the Microbiome-Skin-Brain Axis in Stress and Depression: Fingerprinting of Highly Volatile Compounds in Axillary Sweat via Gas Chromatography-Ion Mobility Spectrometry

Difficulty in the diagnosis of high stress and depression has been recognized conventionally depending on the observation of patient symptoms and psyc...

Elucidating Emotional Patterns in Autism Spectrum Disorder: BERT-Based Analysis Reveals Novel Dimensional Structure

Autism spectrum disorder (ASD) is associated with difficulties in emotion recognition and regulation, which complicates clinical support and treatment...

Development and validation of genomic biotypes for schizophrenia susceptibility from multiple polygenic scores

Understanding the genetic architecture of schizophrenia (SCZ) is invaluable for the development of personalized treatment. In three independent cohort...

Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major Depression

Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled tr...

Signal detection in the psychotic phenotype: Increased sensory precision and reduced decision threshold associated with psychotic-like experiences

Psychotic-like experiences may reflect disrupted signal detection, whereby individuals detect signals in noisy input that are unlikely to be present. ...

Comparing Machine and Deep Learning Models for Pediatric Anxiety Classification using Structured EHRs and Area-based Measures of Health Data

This study investigates the performance of various machine learning (ML) and deep learning (DL) models to classify pediatric patients at risk of anxie...

Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction

Polygenic scores (PGSs) have emerged as promising tools for predicting complex traits from genetic data, however, their predictive performance for psy...

ROC Analysis of Biomarker Combinations in Fragile X Syndrome-Specific Clinical Trials: Evaluating Treatment Efficacy via Exploratory Biomarkers

Fragile X Syndrome (FXS) is a rare neurodevelopmental disorder caused by a trinucleotide repeat expansion on the 5’ untranslated region of the FMR1 ge...

Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression

Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood...

Towards Richer AI-Assisted Psychotherapy Note-Making and Performance Benchmarking

Psychotherapy note-making is crucial for effective patient care. However, traditional formats such as SOAP (Subjective, Objective, Assessment, and Pla...

COVID-19 modulates pregnancy outcomes

The COVID-19 pandemic exposed many pregnant individuals to SARS-CoV-2. Literature suggests a link between gestational COVID-19 and adverse gestational...

User Experience and Therapeutic Alliance in AI-Driven Mental Health Interventions: A Protocol for a Systematic Review of Qualitative Studies

Artificial intelligence (AI) technologies are increasingly being integrated into mental health interventions, but their impact on user experience and ...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data

Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrim...

Mindfulness-Based Interventions using Artificial Intelligence: A Systematic Review Protocol

Mindfulness-based interventions (MBIs) have gained significant recognition as effective approaches for promoting mental health and well-being. With ra...