Learning Infection Risk in Asplenia from Structured and Time-Series Clinical Records
Bibliographic record
Abstract
Asplenic patients face an elevated risk of severe infections and other adverse health outcomes due to impaired immune function. Accurate prediction of these risk events is essential for enabling timely and personalized clinical interventions. In this study, we propose and evaluate multiple strategies for representing temporal clinical data extracted from Electronic Health Records, with the aim of predicting infection onset in asplenic patients. The first strategy leverages embedding-based representations to capture temporal dependencies within sequences of clinical events. The second adopts a more traditional approach, constructing an occurrence matrix that encodes the presence or absence of events without accounting for their temporal order. All approaches were assessed using a gradient boosting classifier (LightGBM), and their predictive performance was systematically compared. This comparative analysis highlights the strengths and limitations of temporal versus non-temporal data representations for clinical risk prediction, offering valuable insights into their applicability within precision medicine frameworks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".