A Framework for Privacy Assurance and Ubiquitous Knowledge Discovery in Health 2.0 Data Mashups
Bibliographic record
Abstract
Knowledge discovery is a critical component in improving health care. Health 2.0 leverages Web 2.0 technologies to integrate and share data from a wide variety of sources on the Internet. There are a number of issues which must be addressed before knowledge discovery can be leveraged effectively and ubiquitously in Health 2.0. Health care data is very sensitive in nature so privacy and security of personal data must be protected. Regulatory compliance must also be addressed if cooperative sharing of data is to be facilitated to ensure that relevant legislation and policies of individual health care organizations are respected. Finally, interoperability and data quality must be addressed in any framework for knowledge discovery on the Internet. In this chapter, we lay out a framework for ubiquitous knowledge discovery in Health 2.0 based on a combination of architecture and process. Emerging Internet standards and specifications for defining a Circle of Trust, in which data is shared but identity and personal information protected, are used to define an enabling architecture for knowledge discovery. Within that context, a step-by-step process for knowledge discovery is defined and illustrated using a scenario related to analyzing the correlation between emergency room visits and adverse effects of prescription drugs. The process we define is arrived at by reviewing an existing standards-based process, CRISP-DM, and extending it to address the new context of Health 2.0.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".