Mined-Knowledge and Decision Support Services in Electronic Health
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
Large organizations in various information domains are constantly facing the challenges of growing size, new business requirements, and customer demands for service agility. As an example, in the healthcare domain provision of unique electronic health record systems (EHR) for patient identification and health history, integration of regional systems into a nation-wide system, information and service sharing, and security and privacy of patient data have generated a set of new challenges. Canada Health In-foway has proposed an information infrastructure for networked healthcare systems that is based on service oriented architecture (SOA) and provides standards for sharing data and services. In this paper, we investigate the provision of mined-knowledge (results of data mining on patient data), clinical decision support systems, and network visualization and monitoring through SOA. We also address the advantages of SOA implementation using an enterprise service bus in order to accommodate these services. Such services can benefit similar domains such as banking, communications, air traffic control, and transportation.
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.
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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it