Big data in healthcare: Conceptual network structure, key challenges and opportunities
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
Big data is a concept that deals with large or complex data sets by using data analysis tools (e.g., data mining, machine learning) to analyze information extracted from several sources systematically. Big data has attracted wide attention from academia, for example, in supporting patients and health professionals by improving the accuracy of decision-making, diagnosis and disease prediction. This research aimed to perform a Bibliometric Performance and Network Analysis (BPNA) supported by a Scoping Review (SR) to depict the strategic themes, thematic evolution structure, main challenges and opportunities related to the concept of big data applied in the healthcare sector. With this goal in mind, 4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software. The bibliometric performance showed the number of publications and citations over time, scientific productivity and the geographic distribution of publications and research fields. The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density. The thematic evolution structure presented the most important themes and how it changes over time. Lastly, we presented the main challenges and future opportunities of big data in healthcare.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it