A novel self-adaptive method for improving patient monitoring with composite early-warning scores
Bibliographic record
Abstract
Wearable sensors utilize small, low-cost, noninvasive, and wireless components. These sensors capture vital signs, allowing the monitoring of patients remotely. In this manner, they are efficient tools to enhance patient care and can be used to monitor vulnerable populations, and keep track of the development of chronic diseases, and the transmission of infectious illnesses – such as during pandemics. However, there are many challenges to monitoring patients using wearables, with massive data generation and battery power consumption being significant constraints. Strategies to reduce data generation should be applied taking into account the patient’s clinical status and health risks. Previous studies took advantage of single early-warning scores (EWS) utilized in infirmaries to detect emergencies, reduce transmissions, and be a reference for self-adaptive features embedded in the devices. Our work proposes the use of composite EWS to infer health deterioration risk, minimize data transmissions and power consumption, and reduce excessive alarms through self-adaptive features based on these scores. We also compare our method with previous studies using real patient data. Further, we propose applying self-adaptive features to sampling, processing, and transmission rates. Our method demonstrated enhanced data reduction, 81% fewer readings than the baseline, significant pruning of the number of alarms, and dynamic and automatic inference of patient risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".