Characteristics and classification of big data in health care sector
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
Information technology has advanced during the last five decades to the stage where its impact is being felt by the society in every service that it gets from media, business, health care, consumer electronics, energy and power, and transportation domains. During this course of human-technology interaction enormous amount of data and knowledge transfer takes place directly between service providers and their clients, as well as indirectly between clients. Because human tendency is to “analyze” its past in order to predict the “future”, keeping track of this dynamically streaming voluminous heterogeneous data, called Big Data (BD), and analyzing it for meaningful discovery of knowledge that leads to value-added business becomes an important research activity. It is in this context that research in Big Data (BD) computing has emerged. Meaningful decisions can be based only on significant knowledge discovery, which in turn requires a good understanding of the characteristics of the accumulated data, an appropriate classification of this huge collection, and an efficient analysis of it. Health care sector is a critical infrastructure because its services affect the lives of humans and the lack of service continuity may be disastrous to the economy and human lives. The large amount of data collected by this sector from its clients is structured into Electronic Health Records (EHR) which is BD, and is used along with pharmaceutical and regulatory data in providing health services. More BD is generated while administering services and measuring their impacts on clients after administering the services. It is in this larger context that we investigate the types and sources of Health Care BD (HBD), its characteristics, and give a classification of it.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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