Heterogeneous Patient Graph Embedding in Readmission Prediction
Bibliographic record
Abstract
The number of youth seeking mental health services has been increasing in the past decade. Accurate prediction of hospital readmission is a contributing factor in addressing youth mental health problem and healthcare service utilization. Medical records are an important source of information for readmission prediction, however utilizing such records in the context of mental care, requires overcoming two impeding challenges: the diversity of service utilization (e.g., using psychiatric vs. overdose vs. trauma clinics all for the same underlying mental health reason) and the heterogeneity of data associated with each service. Graph Neural Network (GNN) is shown to be promising in performing classification or regression tasks when input data bears a complex structure. In this research, we propose using graph embedding to first generate patient graph that captures episodic emergency department visits and the complex service utilizations of the patient. We then use GNN for readmission prediction. For embedding and training purposes, we utilize more than 4,000 unique mental health patients data over 19 years. To evaluate our approach we systematically compare a variety of of GNN models with four RNN models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU. Our experimental evaluation demonstrates that encoding the complex interrelationship between features of a patient using graph embedding and GNN improves the performance of the predictive model compared to RNN counterparts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".