Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU
Bibliographic record
Abstract
A neonatal intensive care unit (NICU) provides specialized care for preterm or ill term infants. The onset of many conditions they can develop are not obvious to physicians until they are significantly impacted and this could result in death. An example of such a problem is neonatal infection which is a common cause of death for premature infants. It remains a challenging task for clinicians to accurately diagnose the presence of bacteria on patients with frequent presence of multiple comorbidities. There is potential for early detection of neonatal infections by timely analysis of patient physiological data and this can lead to improved health outcome of critically ill patients. This paper demonstrates application of a method for Temporal Pattern Recognition and Mining (TPRMine) in order to (a) understand if continuous analysis of temporal changes in patient physiological data streams can lead to discovery of pathophysiological patterns from patients at risk of neonatal sepsis and, (b) utilize the resulting analysis for formulating and testing hypothesis facilitating statistical quantification of patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".