An evaluation method for the evaluation of big data based streaming analytic clinical decision support systems
Bibliographic record
Abstract
This thesis presents a methodology for evaluating a scalable clinical decision support systems (CDSS) that uses high frequency streaming physiological data using a holistic approach that includes the presence of population health indicators. The plan applies concepts and uses indicators suggested in the HOT-Fit framework, while applying the evaluation template developed by Public Health Ontario and uses an indicator structure described in York Region Public Health’s Monitoring and Evaluation Framework. The methodology is applied within the research to the implementation of the Artemis Platform at the McMaster Children’s Hospital (MCH) Neonatal Intensive Care Unit (NICU). NICUs, have specific requirements relating to the use of clinical data and the implementation of new IT infrastructure. These requirements predicate the need for informative documentation that describes the utilization of the CDSS including a Privacy Impact Assessment (PIA), Threat and Risk Assessment (TRA), and a research and ethics proposal.
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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 | 0.130 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".