Development of an Early Warning System for Sepsis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it