Psychiatry

Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: a large and longitudinal machine learning cohort study.

BACKGROUND: Depression serves as a prodromal symptom of dementia, and individuals with depression exhibit a significantly higher risk of developing dementia. The aim of this study is to develop and validate a novel dementia risk prediction tool among middle-aged and elderly individuals with depression based on machine learning algorithms.

Association of risk factors with mental illness in a rural community: insights from machine learning models.

BACKGROUND: Mental health conditions, particularly depression and anxiety, are highly prevalent and impose substantial health burdens globally. Despite advancements in machine learning, there is limited application of these methods in predicting common mental illnesses within community populations in low-resource settings.

Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study.

BACKGROUND: Accurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidenc...

Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study.

BACKGROUND: Domestic violence (DV) is a significant public health concern affecting the physical and mental well-being of numerous women, imposing a s...

Peer Relationships Are a Direct Cause of the Adolescent Mental Health Crisis: Interpretable Machine Learning Analysis of 2 Large Cohort Studies.

BACKGROUND: Converging evidence indicates an adolescent mental health crisis in Western societies that has developed and exacerbated over the past dec...

Biological markers and psychosocial factors predict chronic pain conditions.

Chronic pain is a multifactorial condition presenting significant diagnostic and prognostic challenges. Biomarkers for the classification and the pred...

Evaluating Reasoning LLMs for Suicide Screening with the Columbia-Suicide Severity Rating Scale

Suicide prevention remains a critical public health challenge. While online platforms such as Reddit's r/SuicideWatch have historically provided spa...

Reasoning language models for more transparent prediction of suicide risk.

BACKGROUND: We previously demonstrated that a large language model could estimate suicide risk using hospital discharge notes.

Voice biomarkers of perinatal depression: cross-sectional nationwide pilot study report

Perinatal depression (PND) affects 1 in 5 mothers, with 85% lacking support. Digital health tools offer early identification and prevention, potenti...

Mortality and its predictors among people with dementia receiving psychiatric in-patient care.

BACKGROUND: Although dementia is a terminal condition, palliation can be a challenge for clinical services. As dementia progresses, people frequently ...

Construction of disability risk prediction model for the elderly based on machine learning.

The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing di...

Construction and validation of a predictive model for suicidal ideation in non-psychiatric elderly inpatients.

BACKGROUND: Suicide poses a substantial public health challenge globally, with the elderly population being particularly vulnerable. Research into sui...

Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD - insights from EEG tensor decomposition.

BACKGROUND: Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. Wh...

Fair Uncertainty Quantification for Depression Prediction

Trustworthy depression prediction based on deep learning, incorporating both predictive reliability and algorithmic fairness across diverse demograp...

Hippocampal-prefrontal functional neural networks in a rat model of fragile X syndrome are poorly organized with limited resiliency.

Fragile X Syndrome (FXS) is a common cause of autism spectrum symptoms. The genetic mutation results in multiple molecular alterations that are hypoth...

Navigating challenging patient factors in systemic therapy for head and neck cancer.

Patients with head and neck cancer often present with complex challenges due to a substantial comorbidity burden, including substance use disorders, a...

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity wit...

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds ...

Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.

BACKGROUND AND OBJECTIVE: Accurate detection of schizophrenia poses a grand challenge as a complex and heterogeneous mental disorder. Current diagnost...

Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insig...