A Cusum-Based Multilevel Alerting Method for Physiological Monitoring
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
Alerting systems used by current physiological monitors are designed to detect changes in the levels of vital signs, but they tend to be very sensitive to artifacts. This paper proposes a method to detect changes in the direction of trend and generate multilevel alerts according to the statistical significance of the detection. One-point-ahead signal predictions are calculated by averaging the historical data with the weights decreasing in the past. The two-sided cumulative sums (Cusum) of the prediction errors are tested against multiple thresholds to detect change points with two levels of certainty. The temporal shapes of the detected changes are analyzed using heuristics to determine whether to trigger an alert. The method was tested offline using 20 cases collected during surgery at a local hospital. The detection results were evaluated by two experienced anesthesiologists. The direction of trend was correctly detected in 90.2% of the annotated changes for end-tidal carbon dioxide, 89.4% for expiratory minute volume, 91.8% for peak airway pressure, and 95.4% for noninvasive blood pressure. The certainty levels of the true-positive alerts estimated by the algorithm have a high ratio of agreement with the anesthesiologists' evaluations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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