Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders
Bibliographic record
Abstract
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 treated at any level of care at the Children's Hospital of Eastern Ontario Eating Disorders Program (2018-2024) were extracted. Complex clinical course outcome was defined as either readmission after discharge or a treatment trajectory deviating from the expected step-down in intensity, including return to the same or escalation to a higher level of care. Thirty-four intake and discharge variables from the first treatment episode were used to train seven machine learning models and logistic regression using repeated nested cross-validation. Performance was assessed by AUC and brier scores. Models using intake-only versus intake plus discharge data were compared. A parsimonious model using the top 10 predictors was also evaluated. RESULTS: Random forest model with intake and discharge data achieved the best performance (AUC = 0.723; Brier = 0.176) that was significantly superior to logistic regression. Models trained on intake-only data showed poor discrimination (AUCs < 0.6). Including discharge data improved model performance across all algorithms. The most important predictor was weight change throughout treatment. Random forest performance declined when restricted to the top 10 predictors. DISCUSSION: Supervised machine learning demonstrates improved predictive performance for eating disorder disease course outcomes compared to traditional statistical methods, especially in higher-dimensionality settings. These findings support future application of machine learning to complex biopsychosocial datasets to advance precision medicine initiatives in the eating disorder field and better understand the etiology of disease trajectory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".