{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:24:27Z","timestamp":1771068267541,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010835","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000}}],"reference-count":25,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T00:00:00Z","timestamp":1693872000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["1950\/19"],"award-info":[{"award-number":["1950\/19"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"name":"VATAT - council for higher education"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone. Then, they use this mental model to derive the causes for instability and choose appropriate interventions. Not only this is a very hard problem due to the nature of the signals, but it also requires expertise and a clinician\u2019s ongoing presence at the bedside. Clinical decision support tools based on mechanistic dynamical models offer an appealing solution due to their inherent explainability, corollaries to the clinical mental process, and predictive power. With a translational motivation in mind, we developed iCVS: a simple, with high explanatory power, dynamical mechanistic model to infer hidden cardiovascular states. Full model estimation requires no prior assumptions on physiological parameters except age and weight, and the only inputs are arterial and venous pressure waveforms. iCVS also considers autonomic and non-autonomic modulations. To gain more information without increasing model complexity, both slow and fast timescales of the blood pressure traces are exploited, while the main inference and dynamic evolution are at the longer, clinically relevant, timescale of minutes. iCVS is designed to allow bedside deployment at pediatric and adult intensive care units and for retrospective investigation of cardiovascular mechanisms underlying instability. In this paper, we describe iCVS and inference system in detail, and using a dataset of critically-ill children, we provide initial indications to its ability to identify bleeding, distributive states, and cardiac dysfunction, in isolation and in combination.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010835","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T17:32:24Z","timestamp":1693935144000},"page":"e1010835","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["iCVS\u2014Inferring Cardio-Vascular hidden States from physiological signals available at the bedside"],"prefix":"10.1371","volume":"19","author":[{"given":"Neta","family":"Ravid Tannenbaum","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omer","family":"Gottesman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azadeh","family":"Assadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mjaye","family":"Mazwi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uri","family":"Shalit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7684-1429","authenticated-orcid":true,"given":"Danny","family":"Eytan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"issue":"1","key":"pcbi.1010835.ref001","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.mbs.2012.09.003","article-title":"Patient-specific modeling of cardiovascular and respiratory dynamics during hypercapnia","volume":"241","author":"L Ellwein","year":"2013","journal-title":"Mathematical biosciences"},{"issue":"1","key":"pcbi.1010835.ref002","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s00285-012-0535-8","article-title":"A practical approach to parameter estimation applied to model predicting heart rate regulation","volume":"67","author":"MS Olufsen","year":"2013","journal-title":"Journal of mathematical biology"},{"issue":"5","key":"pcbi.1010835.ref003","first-page":"509","article-title":"Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children","volume":"24","author":"A Fanelli","year":"2019","journal-title":"Journal of Neurosurgery: Pediatrics"},{"issue":"1","key":"pcbi.1010835.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-018-0455-y","article-title":"Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them","volume":"17","author":"JG Chase","year":"2018","journal-title":"Biomedical engineering online"},{"issue":"11","key":"pcbi.1010835.ref005","doi-asserted-by":"crossref","first-page":"e204","DOI":"10.1371\/journal.pcbi.0030204","article-title":"From inverse problems in mathematical physiology to quantitative differential diagnoses","volume":"3","author":"S Zenker","year":"2007","journal-title":"PLoS computational biology"},{"issue":"1","key":"pcbi.1010835.ref006","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.artmed.2008.11.009","article-title":"A hybrid hierarchical decision support system for cardiac surgical intensive care patients. 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