AIMC Topic: Mood Disorders
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MT-RCAF: A Multi-Task Residual Cross Attention Framework for EEG-based emotion recognition and mood disorder detection.
Computer methods and programs in biomedicine
May 17, 2025
BACKGROUND AND OBJECTIVE: Prolonged abnormal emotions can gradually evolve into mood disorders such as anxiety and depression, making it critical to study the relationship between emotions and mood disorders to explore the causes of mood disorders. E...
Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.
Computers in biology and medicine
Jul 31, 2024
Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, pass...
Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.
JMIR mHealth and uHealth
Jul 17, 2024
BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide diseas...
Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.
Scientific reports
Jan 22, 2024
Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a...
Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study.
Journal of medical Internet research
Jul 20, 2023
BACKGROUND: Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early pr...
Comparisons of deep learning and machine learning while using text mining methods to identify suicide attempts of patients with mood disorders.
Journal of affective disorders
Aug 24, 2022
BACKGROUND: Suicide attempt is one of the most severe consequences for patients with mood disorders. This study aimed to perform deep learning and machine learning while using text mining to identify patients with suicide attempts and to compare thei...
The current state of memory Specificity Training (MeST) for emotional disorders.
Current opinion in psychology
Feb 11, 2021
Memory Specificity Training (MeST) is an intervention developed from basic science that has found clinical utility. MeST uses cued recall exercises to target the difficulty that some people with emotional disorders have in recalling personally experi...
Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach.
Metabolic brain disease
Jan 7, 2021
Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), stagin...
Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.
Scientific reports
Nov 11, 2020
Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms ...
Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.
BMC psychiatry
Mar 30, 2020
BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional depend...
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