Importance and need of IoMT and big data to revolutionizing healthcare industry
Bibliographic record
Abstract
Digital technology is changing rapidly in the 21st century and all of us are witnessing the swift advancement of digital technology. The stakeholders of the healthcare industry are now advocating the implementation and deployment of digital technology in the healthcare sector. The Internet of Medical Things (IoMT) and big data technology integrating with the cloud or fog computing bring significant revolutions in the healthcare sector for mankind; starting from telemedicine, and cost-efficient healthcare service at remote monitoring to remote surgery. This sector needs a digital technology-enabled multi-facility healthcare center to revolutionize the healthcare industry. Therefore, leveraging IoMT with big data and cloud technology is an ideal solution for effectively revolutionizing the healthcare industry. Even digital technology, IoMT, and cloud/fog technology fulfill the horizons of medical healthcare needs, quite a few important hurdles including the size of the data, variety of data format, noisy data with poor quality, variety of sources of healthcare segmented or warehouse data, processing and analyzing semi-structured and unstructured data, processing and analyzing moving data, data security and privacy, data ownership and governance that need to be addressed before harmonious, secure, acceptable, and malleable solutions are presented to address the healthcare demands. This chapter shows the importance and needs of IoMT and emerging digital technology with big data to revolutionize the healthcare industry. This chapter also focuses on the challenges faced by the healthcare industry in revolutionizing, transforming big data, and adopting IoMT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".