Depression

CLINPREAI: AN AGENTIC AI SYSTEM FOR EARLY POSTPARTUM DEPRESSION RISK PREDICTION FROM MULTIMODAL EHR DATA

Postpartum depression (PPD) affects 10–15% of mothers annually, yet early identification remains challenging. We introduce ClinPreAI, a novel agentic AI system that autonomously designs, implements, and evaluates machine learning solutions for PPD risk prediction using multimodal electronic health record data. We analyzed data from 4,161 pregnant individuals at Texas Children’s Hospital (2012–2025...

Arachnoiditis: Leveraging crowdsourcing and AI in a cross-sectional study of 1,105 cases to improve identification, understanding, and treatment

Arachnoiditis, a painful and potentially disabling neurological condition, results from persistent inflammation of the spinal cord pia-arachnoid membranes following injury. While considered rare, the condition is underdiagnosed. Research on symptomatology, diagnosis, and treatments is scarce, hindering clinical management. Artificial intelligence (AI) offers promising opportunities for rare diseas...

Personalized Machine Learning guided Intervention for Optimizing Lifestyle Behaviors in Depression

Personalized data-driven interventions for depression are much needed. Here, we leveraged N-of-1 machine learning (ML) to optimally target behavioral ...

Classifying and visualizing medication use in the Adolescent Brain Cognitive Development (ABCD) Study

Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...

Bridging Brain Signals and Self-Reported Symptoms: An AI-Driven, High-Sensitivity Model for Detecting Suicidality in Major Depressive Disorder

Major depressive disorder (MDD) with suicidality represents a significant public health concern, as suicide ranks among the leading causes of death wo...

An informatics approach to profiling patient experiences using electronic health records: constructing and clustering the burden space of individuals under 65 years of age with multiple long-term conditions

Living with multiple long-term conditions (MLTC) profoundly impacts patients’ lives, affecting not only their health but also their financial, emotion...

Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden

Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying a...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

Effect and Mechanisms of a Voice-based Coach using AI on Psychological Distress: A Phase 2 Randomized Trial

Artificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, bu...

Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A Multi-Institutional Retrospective Cohort Study

Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electr...

Detecting Mental Disorders in Social Media Using a Transformer-Based Ensemble of Binary Classifiers

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...

Mapping heterogeneity in the neuroanatomical correlates of depression

Major depressive disorder (MDD) affects millions worldwide, yet its neurobiological underpinnings remain elusive. Neuroimaging studies have yielded in...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

Peripheral Metabolic–Redox Signaling as a Core Mechanism of Major Depressive Disorder: Evidence From Deep Metabolomic Phenotyping

Major depressive disorder (MDD) is a neuro-immune, oxidative, and nitrosative stress (NIMETOX) disorder, in which peripheral immune-redox pathways int...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and postpartum depression (PPD), likely through complex...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-section...

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advanc...

Acoustic-based machine learning approaches for depression detection in Chinese university students.

BACKGROUND: Depression is major global public health problems among university students. Currently, the evaluation and monitoring of depression predom...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of the most common mental disorders. Early mental diso...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise r...