Bipolar Disorder

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 practice is not well-studied. To characterize differences in documentation and treatment of psychiatric symptoms in primary care outpatient notes generated using ambient scribes. Case-control electronic health records Primary care annual visit notes from ...

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 trials (RCTs), low drug-placebo differences and high placebo response rates are persistent challenges. An objective biomarker that can prospectively identify which patients will respond to antidepressant or placebo could greatly enhance both clinical c...

Event-based seizure detection in human iEEG with neuromorphic hardware

Epilepsy is a neurological disorder that affects approximately 1% of the global population. The current method for seizure monitoring, seizure diaries...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depression

Steroid hormone profiles in affective disorders suggest hypothalamic– pituitary–adrenal (HPA) axis dysregulation and may reveal novel therapeutic targ...

Resting-State Functional Connectivity of the Fronto-Limbic and Default Mode Networks as Predictors of Antidepressant Response in Major Depressive Disorder

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet treatment response to antidepressants remains highly variable, with a ...

Developing an AI-Enhanced Individualized Prediction Tool for Psychopathological Symptoms in Vietnam: A Study Protocol

Artificial intelligence (AI) is increasingly leveraged in mental healthcare for early detection, monitoring, and personalized intervention. However, m...

Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home

There is a growing interest in performing automated, longitudinal tracking of sleep in the home environment using wearables and machine learning. Wear...

Predicting Olanzapine Induced BMI increase using Machine Learning on population-based Electronic Health Records

Weight gain is a common side effect in patients treated with olanzapine (N05AH03), contributing to increased risks of metabolic complications such as ...

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked populati...

Ecological Assessment of Transdiagnostic Clinical Symptoms in Serious Mental Illness with Daily Smartphone Surveys

Clinical symptoms in serious mental illness (SMI) fluctuate dynamically, yet standard interview-based assessments often fail to capture these daily ch...

Development, System Design, Safety, and Performance Metrics of a Conversational Agent for Reducing Depressive and Anxious Symptoms Based on a Large Language Model: The MHAI Study

Conversational agents based on large language models (LLMs) have shown moderate efficacy in reducing depressive and anxiety symptoms. However, most ex...

Predictive Modelling of Depression Treatment Response using Individual Symptoms and Latent Factors

Machine learning models have increasingly been used to identify predictors of treatment response in depression, and it is hoped that they may eventual...

Leveraging fMRI non-stationarity for deep learning classifier training and feature detection to improve schizophrenia diagnosis

A neurobiologically-based diagnosis with superior reliability in place of clinical interview-based diagnosis is a primary goal in psychiatry. Dynamic ...

KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes

Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseas...

Predicting Relapse and Psychosocial Functioning in Major Depressive Disorder: A Machine Learning Approach Using Clinical and Resting-State fMRI Data

Machine learning approaches pave a promising avenue to advance individual predictions about psychiatric illnesses, possibly using biomarkers. Here, we...

Leveraging Reddit data for Context-enhanced Synthetic Health Data Generation to Identify Low Self Esteem

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured too...

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their firs...

EEG-Based Prediction of rTMS Treatment Response in Depression: Nonlinear Features and Machine Learning with Minimal Electrode

Repetitive transcranial magnetic stimulation (rTMS) is an established intervention for treatment-resistant depression, but response rates remain highl...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine and Risperidone in Alzheimer’s Disease: A Real□World Cohort Study with Treatment Effect Heterogeneity Analysis

Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...