What question did this study set out to answer?

Identify and interpret the key factors driving mood state deterioration or amelioration in youth with pre-existing psychiatric or developmental disorders during COVID-19 lockdowns.

November 7, 2023BioMedInformaticsOpen Access

Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

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Authors

CNCharis NtakoliaHellenic Air Force DPDimitrios PriftisUniversity Mental Health Research Institute KKKonstantinos KotsisUniversity of Ioannina

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Implication

Machine learning study reveals primary predictors of mood state shifts in youth with psychiatric disorders during lockdowns, highlighting pandemic-induced stress as a dominant factor.

Key Points

Identify and interpret the key factors driving mood state deterioration or amelioration in youth with pre-existing psychiatric or developmental disorders during COVID-19 lockdowns.
Implemented an explainable machine learning pipeline using multiple classifiers to analyze mood state trajectories in a clinical youth cohort in Greece.
Evaluated classifier performance and applied model explainability techniques across 13 selected predictive features.
The Random Forest classifier demonstrated the highest predictive performance, achieving a peak AUC-ROC of 76% across 13 features.
Explainability analysis indicated that stress and positive changes stemming from pandemic restrictions served as the top two contributing factors influencing mood changes.

Cite This Study

Ntakolia et al. (2023) studied this question.

synapsesocial.com/papers/69ff91b3b124fe58198580c0 https://doi.org/10.3390/biomedinformatics3040062

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Implication

Machine learning study reveals primary predictors of mood state shifts in youth with psychiatric disorders during lockdowns, highlighting pandemic-induced stress as a dominant factor.

Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

Discussion

Member takes

Implication

Key Points

Cite This Study

Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

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Implication

Key Points

Cite This Study

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