HAIKU: A Semantic Framework for Surveillance of Healthcare-Associated Infections
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
Healthcare-Associated Infections (HAI) impose a substantial health and financial burden. Surveillance for HAI is essential to develop and evaluate prevention and control efforts. The traditional approaches to HAI surveillance are often limited in scope and efficiency by the need to manually obtain and integrate data from disparate paper charts and information systems. The considerable effort required for discovery and integration of relevant data from multiple sources limits the current effectiveness of HAI surveillance. Knowledge-based systems can address this problem of contextualizing data to support integration and reasoning. In order to facilitate knowledge-based decision making in this area, availability of a reference vocabulary is crucial. The existing terminologies in this domain still suffer from inconsistencies and confusion in different medical/clinical practices, and there is a need for their further improvement and clarification. To develop a common understanding of the infection control domain and to achieve data interoperability in the area of hospital-acquired infections, we present the HAI Ontology (HAIO) to improve knowledge processing in pervasive healthcare environments, as part of the HAIKU (Hospital Acquired Infections - Knowledge in Use) system. The HAIKU framework assists physicians and infection control practitioners by providing recommendations regarding case detection, risk stratification and identification of diagnostic factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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