Development of an Early Warning System for Sepsis
Bibliographic record
Abstract
Sepsis is a life-threatening condition that is caused by infection, and is estimated to affects an estimated 1.7 million adults in the United States and contributes to 265,000 deaths annually. Identifying sepsis before it happens and treating it earlier leads to decreased mortality and decreased lengths of stay. As part of the PhysioNet/Computing in Cardiology Challenge 2019, we developed an ensemble-based approach for the early detection of sepsis in ICU patients.Our final model predicted sepsis using the previous 24 hours of data, and consisted of a combination of two con-volutional neural networks and a random forest trained on different subsets of the data. In training our models, we experimented with random undersampling and cluster-based undersampling as a means for addressing severe class imbalance. On validation data, our final model achieved a utility score of 0.432 on hospital A (AUROC: 0.794, AUPRC: 0.101), 0.247 on hospital B (AUROC: 0.816, AUPRC: 0.056), and a utility of 0.375 on combined data from both hospitals (AUROC: 0.809, AUPRC: 0.089). On the heldout test data, the model obtained a utility score of 0.266 and we received an official ranking of 31/79.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".