ArticleThe International journal of eating disorders2021
Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach.
Article in The International journal of eating disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- Comparative Analysis of Regularised Logistic Regression and Random Forest Models for In-hospital or 30-day Post-Discharge Mortality Prediction within the Hospital Standardised Mortality Ratio Framework.Healthcare informatics research · 2026Article
- Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.The International journal of eating disorders · 2026Article
- Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.Asia-Pacific journal of oncology nursing · 2025Article
- Individual-Specific Animated Profiles of Mental Health.Perspectives on psychological science : a journal of the Association for Psychological Science · 2025Article
- Predicting anorexia nervosa treatment efficacy: an explainable machine learning approach.Journal of eating disorders · 2025Article
- Predictive assessment of eating disorder risk and recovery: Uncovering the effectiveness of questionnaires and influencing characteristics.Computational and structural biotechnology journal · 2025Article
- Development of transdiagnostic clinical risk prediction models for 12-month onset and course of eating disorders among adolescents in the community.The International journal of eating disorders · 2023Article
- Predictors of Treatment Outcome in Eating Disorders: A Roadmap to Inform Future Research Efforts.Current psychiatry reports · 2023Review
- Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach.Frontiers in psychiatry · 2023Article
- Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions.Journal of eating disorders · 2022Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
objectivePatterns of response to eating disorder (ED) treatment are heterogeneous. Advance knowledge of a patient's expected course may inform precision medicine for ED treatment. This study explored the feasibility of applying machine learning to generate personalized predictions of symptom trajectories among patients receiving treatment for EDs, and compared model performance to a simpler logistic regression prediction model.
methodParticipants were adolescent girls and adult women (N = 333) presenting for residential ED treatment. Self-report progress assessments were completed at admission, discharge, and weekly throughout treatment. Latent growth mixture modeling previously identified three latent treatment response trajectories (Rapid Response, Gradual Response, and Low-Symptom Static Response) and assigned a trajectory type to each patient. Machine learning models (support vector, k-nearest neighbors) and logistic regression were applied to these data to predict a patient's response trajectory using data from the first 2 weeks of treatment.
resultsThe best-performing machine learning model (evaluated via area under the receiver operating characteristics curve [AUC]) was the radial-kernel support vector machine (AUC DISCUSSION: Prediction of ED treatment response trajectories is feasible and achieves excellent performance, however, machine learning added little benefit. We discuss the need to explore how advance knowledge of expected trajectories may be used to plan treatment and deliver individualized interventions to maximize treatment effects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.