The Kunche Adaptive Estimator: A Reliability Adaptive Kalman Filtering Framework for Autonomous Multi-Biomarker State Estimation in Critical Care Monitoring

The Kunche Adaptive Estimator: A Reliability Adaptive Kalman Filtering Framework for Autonomous Multi-Biomarker State Estimation in Critical Care Monitoring | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 14 November 2025 V1 Latest version Share on The Kunche Adaptive Estimator: A Reliability Adaptive Kalman Filtering Framework for Autonomous Multi-Biomarker State Estimation in Critical Care Monitoring Author : Nikhil Kunche 0009-0008-0736-0136 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176313985.55971071/v1 225 views 82 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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. Supplementary Material File (the kunche adaptive estimator.pdf) Download 3.13 MB Information & Authors Information Version history V1 Version 1 14 November 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords adaptive filtering biomedical signal processing critical care kalman filtering kunche adaptive estimator real-time systems sensor fusion state estimation Authors Affiliations Nikhil Kunche 0009-0008-0736-0136 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 225 views 82 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nikhil Kunche. The Kunche Adaptive Estimator: A Reliability Adaptive Kalman Filtering Framework for Autonomous Multi-Biomarker State Estimation in Critical Care Monitoring. Authorea . 14 November 2025. DOI: https://doi.org/10.22541/au.176313985.55971071/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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