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Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.

Objective: To compare the predictive performance of supervised machine learning models to logistic regression in identifying youth with eating disorders at risk of a complex clinical course based on clinical characteristics from the first treatment episode. Methods: Clinical data from 327 youth trea...

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Published in:International Journal of Eating Disorders Vol. 59; no. 1; pp. 134 - 146
Main Authors: Ryall, Stephanie, Bradley, Abigail, El Emam, Khaled, Obeid, Nicole
Format: Article
Published: Wiley-Blackwell Jan2026
Subjects:
Ontario
Treatment of eating disorders
Body weight
Retrospective studies
Eating disorders
Stature
Anthropometry
Disease risk factors
Adolescence
Risk assessment
Random forest algorithms
Boosting algorithms
Prediction models
Academic medical centers
Body mass index
Logistic regression analysis
Patient readmissions
Symptoms
Children's hospitals
Tertiary care
Descriptive statistics
Longitudinal method
Support vector machines
Medical records
Acquisition of data
Machine learning
Comparative studies
Data analysis software
Hospital care of teenagers
Sensitivity & specificity (Statistics)
Disease progression
Online Access:View this record in EBSCOhost

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View this record in EBSCOhost