Publications by authors named "M John Broulidakis"

Article Synopsis
  • The study utilized machine learning models to identify reliable diagnostic markers for eating disorders, major depressive disorder, and alcohol use disorder, targeting young adults aged 18-25.
  • The classification models showed high accuracy rates (AUC-ROC ranging from 0.80 to 0.92) even without considering body mass index and highlighted shared predictors like neuroticism and hopelessness.
  • Additionally, the models were moderately successful in predicting future symptoms related to eating disorders, depression, and alcohol use in a longitudinal sample of adolescents, indicating the potential for improved diagnosis and risk assessment in mental health.
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Background: Personality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers.

Methods: We explored associations between personality and ED-related mental health symptoms using canonical correlation analyses.

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This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.

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Background: Identifying youths most at risk to COVID-19-related mental illness is essential for the development of effective targeted interventions.

Aims: To compare trajectories of mental health throughout the pandemic in youth with and without prior mental illness and identify those most at risk of COVID-19-related mental illness.

Method: Data were collected from individuals aged 18-26 years ( = 669) from two existing cohorts: IMAGEN, a population-based cohort; and ESTRA/STRATIFY, clinical cohorts of individuals with pre-existing diagnoses of mental disorders.

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This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included smiling, frowning, raising the eyebrows, and squeezing the eyes.

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