Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data: performance and real-time scalability in simulation scenarios.
Objective: Combining mechanistic mathematical models of physiology with quantitative observations using probabilistic inference may offer advantages over established approaches to computerized decision support in acute care medicine. Particle filters (PF) can perform such inference successively as d...
| Published in: | Journal of Clinical Monitoring & Computing Vol. 24; no. 4; pp. 319 - 334 |
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| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Aug2010
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104914928&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104914928 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Aug2010 vid: 24 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104914928 52799613 NLM20676923 2010740283 10.1007/s10877-010-9252-2 NLM20676923 104914928 ppf: 319 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Parallel particle filters for online identification of mechanistic mathematical models of physiology from monitoring data: performance and real-time scalability in simulation scenarios. aug: au: Zenker S Zenker, Sven affil: Department of Anaesthesiology and Intensive Care Medicine, University of Bonn Medical Center, Sigmund-Freud-Str. 25, 53105, Bonn, Germany sug: subj: Computer Simulation Models, Biological Algorithms Cardiovascular System Physiology Pilot Studies Filtration Heart Rate Human Probability Monitoring, Physiologic Statistics and Numerical Data Systems Analysis Venous Pressure Physiology ab: Objective: Combining mechanistic mathematical models of physiology with quantitative observations using probabilistic inference may offer advantages over established approaches to computerized decision support in acute care medicine. Particle filters (PF) can perform such inference successively as data becomes available. The potential of PF for real-time state estimation (SE) for a model of cardiovascular physiology is explored using parallel computers and the ability to achieve joint state and parameter estimation (JSPE) given minimal prior knowledge tested.Methods: A parallelized sequential importance sampling/resampling algorithm was implemented and its scalability for the pure SE problem for a non-linear five-dimensional ODE model of the cardiovascular system evaluated on a Cray XT3 using up to 1,024 cores. JSPE was implemented using a state augmentation approach with artificial stochastic evolution of the parameters. Its performance when simultaneously estimating the 5 states and 18 unknown parameters when given observations only of arterial pressure, central venous pressure, heart rate, and, optionally, cardiac output, was evaluated in a simulated bleeding/resuscitation scenario.Results: SE was successful and scaled up to 1,024 cores with appropriate algorithm parametrization, with real-time equivalent performance for up to 10 million particles. JSPE in the described underdetermined scenario achieved excellent reproduction of observables and qualitative tracking of enddiastolic ventricular volumes and sympathetic nervous activity. However, only a subset of the posterior distributions of parameters concentrated around the true values for parts of the estimated trajectories.Conclusions: Parallelized PF's performance makes their application to complex mathematical models of physiology for the purpose of clinical data interpretation, prediction, and therapy optimization appear promising. JSPE in the described extremely underdetermined scenario nevertheless extracted information of potential clinical relevance from the data in this simulation setting. However, fully satisfactory resolution of this problem when minimal prior knowledge about parameter values is available will require further methodological improvements, which are discussed. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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