Predictive assessment of eating disorder risk and recovery: Uncovering the effectiveness of questionnaires and influencing characteristics

This study aims to assess the predictive capabilities of various questionnaires in determining the risk of Eating Disorders (ED) and predicting the level of recovery among individuals. Employing machine learning models and diverse datasets, the research focuses on understanding the effectiveness of different questionnaires in providing insights into ED symptoms and recovery outcomes. Additionally, the study seeks to identify the characteristics that significantly influence the recovery process. The investigation aims to contribute valuable information to enhance the diagnostic and monitoring tools used in the field of mental health, particularly concerning ED.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2025-01-01
DOI
https://doi.org/10.1016/j.csbj.2025.03.048
Citations
2
Primary Topic
Eating Disorders and Behaviors
Type
article
Field-Weighted Citation Impact
4.61
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article

Predictive assessment of eating disorder risk and recovery: Uncovering the effectiveness of questionnaires and influencing characteristics

Carlota Las Hayas, Unai Zulaika, Anabela Almeida, Amaia Pikatza-Huerga
2 citations
Computational and Structural Biotechnology Journal
Eating Disorders and Behaviors
4.61
article

Predictive assessment of eating disorder risk and recovery: Uncovering the effectiveness of questionnaires and influencing characteristics

Carlota Las Hayas, Unai Zulaika, Anabela Almeida, Amaia Pikatza-Huerga
article en
2 citations

Abstract

This study aims to assess the predictive capabilities of various questionnaires in determining the risk of Eating Disorders (ED) and predicting the level of recovery among individuals. Employing machine learning models and diverse datasets, the research focuses on understanding the effectiveness of different questionnaires in providing insights into ED symptoms and recovery outcomes. Additionally, the study seeks to identify the characteristics that significantly influence the recovery process. The investigation aims to contribute valuable information to enhance the diagnostic and monitoring tools used in the field of mental health, particularly concerning ED.

Computational and Structural Biotechnology JournalVol. 28
Universidad de Deusto (ES)
Zero hunger
Openalex Percentile: Top 7%
Eating Disorders and Behaviors
4.61
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Predictive assessment of eating disorder risk and recovery: Uncovering the effectiveness of questionnaires and influencing characteristics — Carlota Las Hayas, Unai Zulaika, et al. · Computational and Structural Biotechnology Journal (2025) | TGRS Research Map | TGRS