Detection of Movement toward Randomness by
Applying the Block Decomposition Method to a
Simple Model of the Circulatory System
Victor Iapascurta
Department of Anesthesia and Intensive Care
N. Testemitanu University of Medicine and Pharmacy
165, Stefan cel Mare si Sfant, Bd., MD-2004
Chisinau, Republic of Moldova
viapascurta@yahoo.com
Abstract
It is known that Shannon’s information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. Applicability of these concepts to objects closer to the “clinical end” is less studied. A possible approach consists of encoding the state of a human body system represented by its system dynamics model using matrices and monitoring the behavior of the system through analysis of these matrices, with a potential extrapolation of the results to the clinical setting. This paper presents an attempt at using some concepts and tools, specifically the block decomposition method (BDM), coming from the new emerging field of algorithmic information dynamics, for the management of a patient, especially in the intensive care unit (ICU). It describes some aspects pertaining to the “pre-clinical” incipient stage and tries to outline eventual future clinical application.
Keywords: system dynamics; models; algorithmic information; algorithmic information dynamics; block decomposition method; critical illness; intensive care
Cite this publication as:
V. Iapascurta, “Detection of Movement toward Randomness by Applying the Block Decomposition Method to a Simple Model of the Circulatory System,” Complex Systems, 28(1), 2019 pp. 59–76.
https://doi.org/10.25088/ComplexSystems.28.1.59
