Interoperability of Data and Knowledge in Distributed Health Care Systems
Bibliographic record
Abstract
In this paper we propose a knowledge management framework for distributed health care systems consisting of data- and knowledge-bases that contain patient data and mined knowledge from health care institutions. The framework takes advantage of data mining techniques, enabling technologies and standards to provide decision making support for the health care personnel. The application areas of the new framework range from clinical care to administrative decision support. With the guidance of the health care researchers the available patient data is mined off-line to extract meaningful knowledge from medical data which can be shared with other institutions through XML-based documents (known as PMML) to achieve knowledge interoperability among different heterogeneous health care systems. Data interoperability is achieved through an XML-based clinical data representation standards (HL7 CDA) that is used to encode patient data. A clinical guideline and a logic module will receive inputs form both PMML and CDA documents to enable decision making at a higher level based on patient data and the mined knowledge. We also applied the proposed framework on three clinical case studies
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 | 0.000 | 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 | 0.000 | 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 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".