AIMC Topic: Depression

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A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.

Schizophrenia research
Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive, negative and depressive symptoms and reported difficulties differentiating between these symptom do...

Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.

Evidence-based mental health
BACKGROUND: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could ...

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

Journal of medical Internet research
BACKGROUND: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts...

Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

The primary care companion for CNS disorders
OBJECTIVE: Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these interactions in dynamical networks and in a linear way over time depending on the severity of depressio...

Robotic gait training in multiple sclerosis rehabilitation: Can virtual reality make the difference? Findings from a randomized controlled trial.

Journal of the neurological sciences
Gait, coordination, and balance may be severely compromised in patients with multiple sclerosis (MS), with considerable consequences on the patient's daily living activities, psychological status and quality of life. For this reason, MS patients may ...

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

American journal of epidemiology
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood esti...

Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

PloS one
BACKGROUND: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research w...

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

European psychiatry : the journal of the Association of European Psychiatrists
BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depress...

Reflections of Low-Income, Second-Generation Latinas About Experiences in Depression Therapy.

Qualitative health research
Depression is higher among second-generation Latinas compared with immigrants, but mental health treatment is stigmatized. Therefore, second-generation Latinas were interviewed after completing an eight-session depression treatment program to gain in...