Data and Mined-Knowledge Interoperability in eHealth Systems
Bibliographic record
Abstract
Data Mining in Medical and Biological Research 160 seamlessly to allow healthcare professionals and administration to use the available services effectively and efficiently. Also, we pay particular attention to the current research activities and issues regarding to the interoperbility of information and knowledge extracted from data mining operations. We propose a new architecture for interoperability of data and mined-knowledge (knowledge extracted from data mining algorithms). Finally, we propose new research avenues on the combination of data mining, eHealth, and service oriented architecture and discuss their characteristics. The structure of this chapter is as follows: Section 2 presents the application of data mining in healthcare. Section 3 introduces different forms of knowledge representations in medical domain. Section 4, describes messaging standards in this domain. After these introductions to knowledge and messaging standards, we propose our framework in Section 5. In Section 6 an architecture for the framework is discussed and finally in Section 7, some research avenues are elaborated for applying our framework on the architecture that is explained in Section 6. We conclude the discussion of the chapter in Section 8.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".