SDTDMn0 : a multidimensional distributed data mining framework supporting time series data analysis for critical care research
Bibliographic record
Abstract
Premature birth is one of the major perinatal health issues across the world. In 2007, the\nestimated Canadian preterm birth rate was 8.1 % (CIHI, 2009). Recent research has shown that\nconditions, such as nosocomial infections or apnoeas, exhibit certain variations in the baby's\nphysiological parameters which can indicate the onset of the event before it can be detected by\nphysicians and nurses. Neonatal Intensive Care Units are some of the highest information\nproducing areas in hospitals. The multidimensional and distributed nature of the data further adds\nanother layer of complexity as physiological changes can occur in one data stream or can be\ncross-correlated between several streams. With the collection and storage of electronic data\nbecoming a global trend, there is an opportunity to analyse the collected data in order to extract\nmeaningful information and improve healthcare. The aforementioned properties of the data\nmotivate the need for a framework that supports analysis and trend detection in a\nmultidimensional and distributed environment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".