From inverse problems in mathematical physiology to quantitative differential diagnoses.

The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experiment...

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Autores principales:	Sven Zenker, Jonathan Rubin, Gilles Clermont
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2007
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c11394659534408980e004d5cfa1f7a3
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spelling	oai:doaj.org-article:c11394659534408980e004d5cfa1f7a32021-11-25T05:41:03ZFrom inverse problems in mathematical physiology to quantitative differential diagnoses.1553-734X1553-735810.1371/journal.pcbi.0030204https://doaj.org/article/c11394659534408980e004d5cfa1f7a32007-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.0030204https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information.Sven ZenkerJonathan RubinGilles ClermontPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 3, Iss 11, p e204 (2007)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sven Zenker Jonathan Rubin Gilles Clermont From inverse problems in mathematical physiology to quantitative differential diagnoses.
description	The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information.
format	article
author	Sven Zenker Jonathan Rubin Gilles Clermont
author_facet	Sven Zenker Jonathan Rubin Gilles Clermont
author_sort	Sven Zenker
title	From inverse problems in mathematical physiology to quantitative differential diagnoses.
title_short	From inverse problems in mathematical physiology to quantitative differential diagnoses.
title_full	From inverse problems in mathematical physiology to quantitative differential diagnoses.
title_fullStr	From inverse problems in mathematical physiology to quantitative differential diagnoses.
title_full_unstemmed	From inverse problems in mathematical physiology to quantitative differential diagnoses.
title_sort	from inverse problems in mathematical physiology to quantitative differential diagnoses.
publisher	Public Library of Science (PLoS)
publishDate	2007
url	https://doaj.org/article/c11394659534408980e004d5cfa1f7a3
work_keys_str_mv	AT svenzenker frominverseproblemsinmathematicalphysiologytoquantitativedifferentialdiagnoses AT jonathanrubin frominverseproblemsinmathematicalphysiologytoquantitativedifferentialdiagnoses AT gillesclermont frominverseproblemsinmathematicalphysiologytoquantitativedifferentialdiagnoses
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From inverse problems in mathematical physiology to quantitative differential diagnoses.

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