Modeling and physiological systems
What is Model
copyright by
Dr. Zeinab Adam Mustafa, PhD, M. Sc
Biomedical Engineering Department
Email: zenab42000@yahoo.com
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The Real World
• Complex
• Nonlinear
• Nonhomogeneous
• Often discontinuous
• Anisotropic
• Multilayered
• Multidimensional
Biomedical System Complexity Causes
• Non-linearities:
– Many responses have upper and lower
boundaries with different levels of
physiological sensitivity in between.
• Redundancy:
– Many physiological states are the result of
multiple mechanisms pushing and pulling on
the observable response. Redundancy
makes it difficult for a researcher or clinician
to identify important causal mechanisms.
Biomedical System Complexity
- Causes
• Disparate time constants:
– The importance of an observation often depends on the timing of
the protocol. For instance, the control of arterial blood pressure is
a mix of fast-acting neural mechanisms, slow-acting hormonal
mechanisms, and long-term effects of body fluid volume and
compositions.
• Individual variation:
– Physiological responses are a qualitative and quantitative function
of gender, age, body composition, and other individualities.
• Emergence:
– Many high-level, integrative behaviors of the biological system
cannot be described by the sum of the respective inputs from
basic processes.
Hospital Information Systems
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The System of Interest
• The system of interest is isolated from the
rest of the world by means of a boundary.
• The system is then conceptually reduced to
that of a mathematical model by using a set
of simplifying assumptions.
• Therefore, the model results have significant
limitations and are valid only in the regions
where the assumptions are valid.
What is model
• A model is an imitation or an approximate
representation of prototype.
• Prototype mean:
• Concept, an object , a system , or a
process
What is model
• There are two different ways in which a
system or a process might be studied:
• Experiment with the acutal thing.
• AND
• Experiment with the model of acutal thing.
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What is model
• Graphical, mathematical (symbolic), physical, or
verbal representation or simplified version of a
concept, phenomenon, relationship, structure,
system, or an aspect of the real world.
• The objectives of a model include (1) to facilitate
understanding by eliminating unnecessary
components, (2) to aid in decision making by
simulating 'what if' scenarios, (3) to explain,
control, and predict events on the basis of past
observations.
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Types of model
• Most models fall in one of the two categories of:
• Physical : modeling representation is physical
(example , model of airplane made of wood)
• and Symbolic models, model of a passive
muscle this include a mechanical spring to
represent the elasticity of the muscle and
dashpot to represent its frictional phenomenon
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Types of model
• A model is said to be a symbolic (or
formal)
• Representation is theoretical , or symbolic
• Example of symbolic model (drawing
,flow chart, logical , computer program)
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Types of model
• Mathematical models are abstract
expressions of the relationships among
system or process variables, Newton’s
laws of motion are example of these
models
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• If the system being studied is so complex
that no representation model can be used
, then the well-known problem reduction
technique , or
• subsystem modeling: the system is
divided into collection of less complex
subsystem .
• Each of these subsystem is modeled , and
over all system model is constructed by
linking the subsystem models
appropriately
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Types of model
• Deterministic vs. Stochastic models
• Deterministic models have no components
that are inherently uncertain, i.e., no parameters
in the model are characterized by probability
distributions, as opposed to stochastic model
• For fixed starting values, a deterministic model
will always produce the same result. A
stochastic model will produce many different
results depending on the actual values that the
random variables take in each realization
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Types of model
• Static vs. Dynamic Models
• Static models are at an equilibrium or
steady state, as opposed to dynamic
models which change with respect to time.
• Continuous vs. Discrete Models
• Differential vs. difference equations
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• Qualitative vs. Quantitative Models
• Qualitative models lead to a detailed,
numerical predicition about responses,
whereas quanlitative models lead to
general descriptions about the responses
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