Big data in healthcare research - how can we address public concerns of privacy
Bibliographic record
Abstract
Big data is an emerging technological field that encompasses massive datasets. Its role in the healthcare field is currently being explored and has the potential to greatly improve healthcare and disease surveillance through pattern analysis of health data. Concerns had by the general public focus primarily on potential breaches of privacy and confidentiality of patient medical health records in the context of research. These concerns relate to the innate characteristics of big data, such as large size and fast data acquisition speed, which increases the risk of breaching confidentiality. Therefore, it is important for physicians to be mindful of privacy concerns and maintain trust as big data becomes more prominent. Doing so is a key factor in building public trust in the use. Understanding strategies and limitations of current practice standards will allow physicians to build on existing guidelines to incorporate the rise of big data. This means prioritizing privacy when handling big data through anonymization, creating safe havens and promoting dynamic informed consent practice standards.
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 | 0.398 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.075 |
| Scholarly communication | 0.038 | 0.072 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".