The Kunche Adaptive Estimator: A Reliability Adaptive Kalman Filtering Framework for Autonomous Multi-Biomarker State Estimation in Critical Care Monitoring
Bibliographic record
Abstract
Critical care monitoring relies on intermittent laboratory biomarker measurements at 6 to 24 hour intervals, creating blind spots during which organ dysfunction progresses undetected. We present the Kunche Adaptive Estimator (KAE), the first reliability adaptive Kalman filtering framework enabling continuous multi-biomarker trajectory monitoring through autonomous adjustment of measurement noise covariance based on real-time sensor reliability metrics. The KAE employs three innovations: inverse reliability scaling R(t) = R0/r(t) for measurement noise adaptation, innovation based adaptive process noise Q(t) = Q0(1 + β·v(t)) for clinical regime change detection, and missing data handling via R = ∞ enabling prediction only updates without special case logic. Comprehensive validation across 12 critical biomarkers spanning cardiac (troponin, sST2), pulmonary (pO2, pCO2, pH), renal (NGAL, cystatin-C), hepatic (ammonia), immune (procalcitonin, IL-6), metabolic (lactate), and stress (copeptin) systems through 144,000 Monte Carlo simulations demonstrates exceptional performance. All biomarkers achieve greater than 90% correlation with ground truth, with 7 exceeding 99% correlation. Comparative analysis shows 30% RMSE reduction versus standard Kalman filtering, 24% versus Extended Kalman Filter, and 34% versus Particle Filter (p < 0.001). Detection latency averages 6 to 11 minutes for critical events versus 12 to 19 minutes for baseline methods, enabling 6 to 12 hour earlier clinical intervention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".