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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Detalles Bibliográficos
Publicado en:	International Journal of Eating Disorders Vol. 59; no. 1; pp. 134 - 146
Autores principales:	Ryall, Stephanie, Bradley, Abigail, El Emam, Khaled, Obeid, Nicole
Formato:	Artículo
Publicado:	Wiley-Blackwell Jan2026
Materias:	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
Acceso en línea:	Ver este registro en EBSCOhost