A Cloud Based Big Data Health-Analytics-as-a-Service Framework to Support Low Resource Setting Neonatal Intensive Care Unit
Bibliographic record
Abstract
Critical care patients are monitored by a range of medical devices collecting high frequency data. New computing frameworks and platforms are being proposed to review and analyze the data in detail. The application of these approaches in a low resource setting is challenged by the approaches used for data acquisition. Software as a Service (SaaS) is a form of cloud computing where a cloud-based software application enables the storage, analysis and visualization of data within the cloud. A subset of SaaS is Health Analytics as a Service (HAaaS), which provides software to support health analytics in the cloud. The objective of this study is to design, implement, and demonstrate an extendable big-data compatible HAaaS framework that offers both real-time and retrospective analysis where data acquisition is not tightly coupled. A data warehousing framework is presented to facilitate analysis within a low resource setting. The framework has been instantiated in the Artemis platform within the context of the Belgaum Children Hospital (BCH) case study. Initial end-to-end testing with the Nellcor monitor (bedside monitor at BCH), which was not connected to any human, was completed. This testing confirms the functionality of the new Artemis cloud instance to receive data from test device using an alternate data acquisition approach.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".