Mortality prediction with self normalizing neural networks in intensive care unit patients
Bibliographic record
Abstract
Mortality prediction of intensive care unit (ICU) patients is challenging and important in clinical decision making. Traditionally, severity of illness (SOI) scores are used for predicting mortality. While many SOI scores have been proposed, they tend to underperform on validation. In this work, we investigate Deep Learning (DL) methods focusing on the self normalizing neural network (SNN) for predicting mortality in ICU patients. We evaluate the prediction model on approximately 17150 patients from the MIMIC II dataset. The primary outcomes were 30 days and hospital mortality. Compared to the existing methods in the literature, DL models resulted in superior or comparable predictive performance. The final calibrated SNN resulted in an AUC of 0.8445 (±0.08) for 30 days mortality and 0.86 (±0.12) for hospital mortality. This study warrants further application of DL to prediction problems in the ICU.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".