AIMC Topic: Depression

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Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.

BMC psychology
OBJECTIVES: COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on...

Using machine learning to develop a five-item short form of the children's depression inventory.

BMC public health
BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is...

Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning.

Acta psychologica
OBJECTIVE: A plethora of studies have unequivocally established the profound significance of harmonious familial relationships on the psychological well-being of the elderly. In this study, we elucidate the intergenerational relationships, probing th...

The therapeutic effectiveness of artificial intelligence-based chatbots in alleviation of depressive and anxiety symptoms in short-course treatments: A systematic review and meta-analysis.

Journal of affective disorders
BACKGROUND: The emergence of artificial intelligence-based chatbot has revolutionized the field of clinical psychology and psychotherapy, granting individuals unprecedented access to professional assistance, overcoming time constraints and geographic...

The Effect of Social Robots on Depression and Loneliness for Older Residents in Long-Term Care Facilities: A Meta-Analysis of Randomized Controlled Trials.

Journal of the American Medical Directors Association
OBJECTIVES: Depression and loneliness are challenges facing older residents living in long-term care facilities. Social robots might be a solution as nonpharmacologic interventions. The purpose of this study was to explore the effects of concrete for...

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

Computers in biology and medicine
OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist...

Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic.

IEEE journal of biomedical and health informatics
People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread o...

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.

Journal of affective disorders
BACKGROUND: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-re...

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

PloS one
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promis...