Incoporating Data Mining Applications into Clinical Guildelines
Bibliographic record
Abstract
Computer based clinical guidelines have been developed to help caregivers in practicing medicine. GLIF3 (Guideline Interchange Format 3) is one of several standards that specify the structure for defining guideline models. The decision making steps within the GLIF3 guidelines are limited to evaluation of basic logic expressions. On the other hand, data mining analyses aim at building descriptive or predictive mining models that contain valuable knowledge. However, this knowledge can not be represented using the current guideline specification standards. In this paper, we focus on encoding and sharing the results obtained from a data mining study in the context of clinical care and hence to make it available at the point of care. For this purpose, we investigate available standards to encode the mining results in an interoperable manner and then elaborate on how to incorporate them in the context of guideline-based clinical decision support systems, and in particular GLIF3 models. Finally, we demonstrate the proposed approach using a developed prototype tool for modeling and execution of knowledge-assisted guideline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".