A Knowledge Management Based Approach for Mortality Prediction in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
The Neonatal Intensive Care Unit (NICU) is one of the most information sensitive environments where efforts are made continuously to deliver the optimal health-care for fragile, critically ill patients. NICU health care providers employ cutting edge clinical processes, technologies, and latest tools and techniques to provide care for critically ill new born infants. Research has shown that predicting an infant's mortality is important in making critical care decisions. Contemporary, 21st century neonatal intensive care involves the active participation of parents. Although, there are neonatal scores that can be used to measure severity of illness, they are complex and are difficult to comprehend for a novice. Hence, there is a need to combine all the care related information to obtain an indication of the newborn infant's state of health. A new score for Neonatal Mortality Prediction (NMPS) is proposed in this paper. This NMPS would provide an easy to understood numeric value that can be comprehended by both neonatal health care providers and parents. The NMPS would employ factors captured from the antenatal, perinatal and neonatal periods for estimating a consolidated score.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".