AIMC Topic: Depression

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A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS.

Journal of affective disorders
BACKGROUND: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depr...

Effectiveness of AI-Driven Conversational Agents in Improving Mental Health Among Young People: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: The increasing prevalence of mental health issues among adolescents and young adults, coupled with barriers to accessing traditional therapy, has led to growing interest in artificial intelligence (AI)-driven conversational agents (CAs) a...

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.

Alzheimer's research & therapy
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...

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

Nursing research
BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used ...

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

Journal of medical Internet research
BACKGROUND: Accurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidence is often influenced by socioeconomic factors, such as abrupt events or changes, including pandemic...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

Brain and behavior
INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of post-stroke depression is crucial for its timely prevention. The accuracy of machine learning as a pre...

The effect of esketamine on perioperative anxiety and depressive symptoms in patients undergoing total hysterectomy.

The journal of obstetrics and gynaecology research
AIM: This study aimed to evaluate the effect of esketamine on perioperative anxiety and depressive symptoms, acute stress reaction, and serum neurotransmitters in patients undergoing total hysterectomy.

Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.

Saudi medical journal
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.

Exploratory Analysis of Nationwide Japanese Patient Safety Reports on Suicide and Suicide Attempts Among Inpatients With Cancer Using Large Language Models.

Psycho-oncology
OBJECTIVE: Patients with cancer have a high risk of suicide. However, evidence-based preventive measures remain unclear. This study aimed to investigate suicide prevention strategies for hospitalized patients with cancer by analyzing nationwide patie...

[Construction of recognition models for subthreshold depression based on multiple machine learning algorithms and vocal emotional characteristics].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVES: To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression.