Approaching the complexity of biomedical signal processing

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Approaching the complexity of biomedical signal
processing

• An agent-centered perspective
• Part III - Application to Patient Monitoring

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Part III - Application to Patient Monitoring

• 1. Monitoring as a physically distributed problem
  
• 2. Monitoring as a (distributed) cognition issue
• 3. Monitoring as a negotiation problem

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1. - Monitoring as a physically distributed
problem

• Dimitrios G. Katehakis et al. A Distributed,
  Agent-Based Architecture for the Acquisition,
  Management, Archiving and Display of Real-Time
  Monitoring Data in the Intensive Care Unit,
  Technical Report FORTH-ICS / TR-261
• http//www.ics.forth.gr/ICS/acti/cmi_hta/publicati
  ons/technical_reports/tr261/ICU.html
  

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Intensive Care Unitsa physically distributed
environment
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Intensive Care Unitsa physically distributed
environment

• Many variables
• Continuous measurements of electrocardiogram,
  central venous pressure, systemic arterial
  pressures, cardiac output, urine output,
  pulmonary arterial pressures, blood gases, and
  mixed venous saturation
• Measurements made by the ventilator itself
  respiratory rate, tidal volume, peak inspiratory
  pressure, average airway pressure, spontaneous
  minute volume, lung mechanics, oxygen
  consumption, and metabolic rate
• Many interaction / monitoring needs

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System Architecture

• Two types of agents the acquisition agent and
  the monitoring agent. Acquisition agents perform
  data acquisition and feed data to monitoring
  agents, who facilitate data visualization and
  storage

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Agent s role

• The acquisition agent collects data either from
  patient connected sensors or from clinical
  information systems
• Acquired data are kept temporarily on a local
  data store, until they are transmitted to the
  appropriate monitoring agents
• An acquisition agent may have a number of input
  and output channels, each of which can be
  dedicated to a different monitoring agent. The
  acquisition agent is therefore communicating with
  several monitoring agents simultaneously
• The monitoring agents receive data, which are
  stored temporarily in a data repository and are
  visualized through a Graphical User Interface
  (GUI)

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Introducing cognition agents
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2. - Monitoring as a (distributed) cognitive issue

• GUARDIAN -- A prototype intelligent agent for
  monitoring and therapeutics in intensive care
• Barbara Hayes-Roth
• Knowledge System, Laboratory at Stanford
  University
• http//ai.eecs.umich.edu/cogarch2/authors/bhayes-r
  oth.html

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Monitoring as a (distributed) cognitive issue

• The perception - cognition - action problem
• A compromise to be reached between the quality
  and rapidity of reasoning
• Sacrifice the quality of a solution for one that
  meets the deadline
• A quick action of less quality will push off the
  deadline far enough so that a quality solution
  can be found
• Interleave reactive and cognitive behaviours

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Architecture

• Three independent sub-systems cognition,
  perception and action, which
• Operate concurrently and asynchronously
• Communicate through a globally accessable
  communication interface which asynchronously
  relays data among limited size I/O buffers
• the system interact concurrently with subsets of
  the environment,
• thereby increasing performance,
• and reducing the overall complexity each
  subsystem must be able to deal with

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Cognitive system
Perceptual input / Cognitive events
Action parameter/filters
Perceptual input/filters
Fast reflex arcs
Perception agents
Action agents
Perceptual input
Action parameter
Interactive displays
Sensors
Actuators
I/O
Patient, ventilator, human users,
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Perception agent s role interpretation

• The sensor agent s role is to acquire a given
  type of signals, transduce it into an internal
  representation, and holds the results in its I/O
  buffer
• The perception agent s role is to retrieve the
  sensor agent information, to analyze and
  interpret it and transmit the results to the
  Communication Interface
• Example peak inspiratory pressure
• value  high 
• trend  rising 
• relevance to ongoing reasoning tasks  not
  relevant  
• priority  high 

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Perception agent s role focus of attention

• The perception agent s retrieves the sensor
  agent information at some given rate, according
  to given filters
• Rate and filter information is transmitted by the
  cognitive system, according to the current
  reasoning state
• The system is therefore provided with focus of
  attention capabilities
• The more important the data, the more often the
  system will see it
• Conversely, as the buffer size is limited, if the
  cognitive system does not look at the buffer
  often enough, perceptual information may be lost

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Action agent s role action

• The action agent s role is to
• Monitor its input buffer, retrieve intended
  actions, and translate them into executable
  programs of actuator commands
• Control the execution of these programs by
  sending successive commands to its actuator at
  appropriate times 
• Example action dynamically adjust the
  ventilator s settings
• The action agents relieve the reasoning system of
  the computational burden of managing the
  low-level details of action execution

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Action agent s role user interaction

• The action agent s role is also to communicate
  with the external users, in order to
• Recommend other interventions to correct
  diagnosed problems or avoid predicted problems
• Give explanations about the system s current
  monitoring strategy, its reasoning about some
  particular problem, and the biological and
  physical phenomena underlying the patient s
  condition.

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Coordination between perception, reasoning and
action

• The perception, reasoning, and action systems
  work concurrently, in a continuous way
• Information from the environment is perceived
  continuously, even while the system is engaged in
  computationally expensive reasoning tasks
• The system is guaranteed to perceive any critical
  events that occur
• Conversely, the system reasons continuously,
  regardless of the rate of incoming events. Thus,
  it is guaranteed to complete a critical reasoning
  task without interruption, unless it decides to
  attend to a more critical new event

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Coordination between perception/reasoning and
action

• Fast reflex reactions occur across
  perception-action arcs and allow perception to
  drive action directly
• For example, Guardian might automatically sound
  an alarm and deliver a simple explanation
  whenever perceived values of key physiological
  parameters enter critical ranges
• Comparatively slow cognitive reactions involve
  all three systems, with cognition mediating
  actions in response to perception

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Cognitive system s role

• The role of the cognitive system is twofold
• To interpret perceived information from the
  environment, perform the needed reasoning tasks
  and decide what actions to perform (several
  reasoning agents)
• To construct and modify dynamic control plans to
  coordinate the perception, reasoning, and action
  tasks (one control agent)
• These tasks are performed by agents operating
  according to the blackboard model of control

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Cognitive system s Architecture
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Control agent

• The control agent comprises 3 components that run
  sequentially
• The agenda manager uses current perceptual and
  cognitive events and the current control plan to
  identify and rate possible reasoning operations
  it records them on the agenda buffer
• The scheduler takes information from the agenda
  and uses the control plan to select the operation
  that best matches the current plan it records
  that operation on the next operation buffer it
  may also decide to interrupt the agenda
  management to give priority to critical
  operations
• The executor executes the chosen operation,
  producing changes to the global memory new
  perceptual preprocessing parameters or intended
  actions in output buffers new reasoning results
  for ongoing tasks or new control decisions

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Controller agent cycle
Scheduler
Agenda Manager
Executor
Reasoning Agents
Perception/Action Agents
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Cognitive state

• Holds the control information necessary to drive
  control it is comprised of three buffers
• The event buffer holds current asynchronously
  arriving perceptual inputs and cognitive events
  produced by reasoning
• The agenda holds currently executable reasoning
  operations - those whose trigger conditions are
  satisfied
• The next operation holds the reasoning operation
  to be executed next

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Cognitive state

• All of these buffers have limited capacity, and
  are exploited according to two criteria
• Best-first retrieval (items that score higher are
  retrieved earlier)
• Worst-first overflow (items that score lower
  overflow earlier)
• defined in terms of four orthogonal attributes
• Relevance to Guardian's current reasoning
  activities
• Importance with respect to Guardian's global
  objectives
• Recency of entering the buffer
• Urgency of processing the item in order to have
  the intended effect (e.g., meet deadlines)
• If too many critical events occur simultaneously,
  they will overflow the buffers

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The control plan

• A control plan is a temporal pattern of control
  decisions, each describing a class of operations
  to be performed, under specified constraints,
  during some time period
• It is used to focus the reasoning cycle given a
  strategy to complete the task at hand
• This includes determining which actions have
  priority on the agenda, when the scheduler should
  interrupt, and what the perceptual filters should
  contain
• The only changes to the control plan are those
  made by the control KS, and hence were determined
  necessary by the control plan and environment at
  that time

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Cognitive systems Architecture
Control KS
Reasoning KS
Control agent
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Example Control Plan

• Example plan
• Respond to critical events
• Monitor all parameters for changes
• D 10D 2D
• Time
• According to the first decision, Guardian decides
  to respond to critical events. With the second
  decision, it decides that the perceptual
  preprocessor should send new values for patient
  parameters only when their values change by a
  threshold percentage. These decisions remain in
  effect (with some changes in preprocessing
  threshold) throughout the given period of time

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Example Control KS

• Name Urgent-Reaction
• Trigger Critical observation, O
• Action Record control decision with
• Prescription Quickly react to O
• Criticality Criticality of O
• Goal Diagnosed problems related to O are
  corrected
• This operation is triggered and its parameter, O,
  is instantiated whenever the perception system
  delivers an observation with high criticality
  (such as high PIP - Peak Inspiratory Pressure)
• When executed, it generates a control decision
  favoring  quick  reasoning operations that
   react to  O, and gives it the same criticality
  as O
• The decision is deactivated when its goal is
  achieved, namely that all diagnosed problems
  related to O have been corrected.

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Resulting control plan

• Modified plan
• Respond to critical events
• Monitor all parameters for changes
• D 10D 2D
• Quickly react to high PIP
• Time

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Reasoning knowledge a multispecialist approach

• Reasoning knowledge is distributed among several
  task-dependent specialists
• Diagnosis of observed signs and symptoms
• Prediction of patient condition
• Causal inference of precursors and consequences
  of observations, problems, etc.
• Explanation of underlying causal phenomena
• Each of these tasks may be performed using
  associative or model-based reasoning methods

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Associative methods

• Associative methods use clinical knowledge, apply
  to familiar problems, and give simple
   answers , with minimal explanation
• For example, Guardian responds to an observed
  rise in PIP by quickly diagnosing a
  hypoventilation problem and increasing the
  patient's ventilation
• Having relieved the symptoms and extended the
  hard deadline, it acquires additional data to
  diagnose and correct the specific underlying
  problem (e.g., pneumothorax)

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Model-based methods

• Model-based methods use biological and
  first-principles knowledge, apply to familiar and
  unfamiliar problems, and give detailed
   answers  with informative explanations
• For example, Guardian can give a
  pathophysiological explanation of its prediction
  that normal minute ventilation of a cold
  post-operative patient will result in low
  arterial partial pressure of CO2
• The patient's low temperature leads to decreased
  metabolic activity in the cells, this results in
  decreased O2 consumption and decreased CO2
  production in the tissue compartment.

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Model-based methods

• Another example
• Name Find-Generic-Causes
• Trigger Observe condition C
• where C exemplifies Generic-fault F
• Action Find Generic-fault that can-cause F
• Find-Generic-Causes is triggered when C is
  observed
• Upon execution, the action is to look for
  generic-faults that  can-cause  F
• By recording each such cause in the global
  memory, this operation creates internal events
  that trigger other reasoning operations

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An illustrative scenario

• A scenario illustrating the system capacity to
• Manage moderately important, slowly evolving
  problems (e.g., low temperature and its
  consequences)
• Manage time-critical problems (e.g., high PIP and
  the underlying pneumothorax)

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A strategy to investigate a patient s low
temperature

• The system is monitoring all patient parameters
  for value changes of a threshold percentage
• It notices the patient s low temperature, a
  non-critical problem but worth investigating
• It makes control decisions that instantiate an
  abstract strategy for investigating this type of
  problem
• a) Diagnose the low temperature
• b) Infer and correct immediate consequences
• c) Predict changes
• d) Infer and act to avoid expected consequences

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• a) Diagnose the low temperature
• Attribute the low temperature to the patient s
  immediate post-operative status
• b) Infer and correct immediate consequences
• Infer that the patient's PaCO2 is currently low,
  due to the interaction between low temperature
  and normal breathing rate
• c) Predict changes
• Predict that the temperature will rise to high
  and then fall to normal over several hours
• Predict that the PaCO2 will rise to high and fall
  to normal with temperature
• d) Infer and act to avoid expected consequences
• Decide to lower the breathing rate to correct the
  PaCO2
• Plan a series of rate changes correlated with
  temperature to maintain the PaCO2 within an
  acceptable range

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An unexpected event

• In the course of this strategy, the system
  observes high, rising PIP, indicating a
  potentially life-threatening condition with a
  deadline for corrective action on the order of
  minutes
• A control decision is made that instantiates an
  abstract strategy for correcting critical
  conditions as quickly as possible
• Fast associative reasoning is favoured to
  diagnose and correct the problem

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A strategy to correct critical conditions

• a) Consider other patient data to diagnose the
  problem class, hypoventilation problem
• b) Advise increasing ventilation so the patient
  will get enough oxygen
• c) Request diagnostic actions auscultation of
  the chest for asymmetric breathing sounds and
  inspection of chest xrays
• d) Diagnose the underlying problem, a
  pneumothorax
• e) Advise insertion of a chest tube to relieve
  the pressure of accumulated air in the chest
  cavity
• f) Predict and confirm the resulting drop in PIP
  
• g) Advise reduction of the breathing rate as
  increased ventilation is no longer necessary
• h) Request a lab test in twenty minutes to
  confirm that blood gases are normal

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Discussion

• Knowledge representation is complex, even for
  simple and well-kown situations it is difficult
  to ensure the order and time of execution of the
  system modules
• There is a number of coefficients and variables
  to adjust
• The basic functions of sensing, reasoning and
  acting are distributed among local agents
  sensing and acting may be engaged in a pure
  reactive way but as well be influenced by the
  reasoning process under development
• The ratio of intra-agent computation to
  inter-agent com-munication is relatively high
• Consistency is ensured by a global control plan
  influencing what the agents tackle and
  constraining their internal decisions

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3. - Monitoring as a negotiation problem

• Anthropic agency a multiagent system for
  physiological processes
• Francesco Amigoni, Marco Dini, Nicola Gatti, and
  Marco Somalvico
• Artificial Intelligence in Medicine Journal,
  Vol. 27, n3, 2003
• Special issue  Software agents in health care 

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The anthropic agency

• Agency a multiagent system as a single machine
  composed of complex components the agents
• Anthropic from the Greek anthropos, namely man
  the agency is employed to model the
  physiological processes of the human being
• An example application the regulation of the
  glucose-insulin metabolism in diabetic patients,
  a process where partially overlapping models of
  glucose level regulation coexist

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Diabetic pathology

• Glucose is one of the bodys main sources of
  energy
• The body regulates the processes that control the
  production and storage of glucose by secreting
  the endocrine hormone, insulin, from the
  pancreatic B-cells
• Type 1 diabetes is characterized by a loss of
  pancreatic beta-cell (B-cell) function and an
  absolute insulin deficiency
• Since insulin is the primary anabolic hormone
  that regulates blood glucose level, this results
  in the inability to maintain blood glucose
  concentrations within physiological limits

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Diabetic pathology

• A long time exposition to very high values of
  blood glucose concentration causes serious
  complications to other body organs
  (cardiovascular and renal system, retina)
• Type 1 diabetics require a continuous supply of
  insulin for survival (multiple daily injections
  or a continuous subcutaneous insulin infusion
  guided by daily blood glucose measurements) in
  order to try and keep the glucose concentration
  under control
• Many factors have to be considered to choose the
  current dose of insulin to inject amount of
  food, current glucose concentration value,
  general physical state

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Diabetic pathology

• In diabetes, there is an uncoupling of blood
  glucose levels and the concentration of insulin
  that prevents the proper regulation of glycemia.
  Instead of a narrow glycemic range, blood glucose
  deviations can extend from hypoglycemia into
  hyperglycemia

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Diabetic pathology

• The main problem in the diabetic pathology is the
  insulin response when the person eats because it
  is when the glucose concentration reaches the
  maximum value
• Another issue is the effect of physical activity
  on the insulin level
• There is a need to keep constant the glucose
  level to sustain the physical activity
• Conversely, physical activity helps regulating
  the glucose level and keeping more sensitive to
  insulin, therefore being able to function with
  less insulin

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Variation of the glucose level when eating
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Purpose of a monitoring system

• To constantly monitor the patient, eg analyze its
  current physiological state
• To inject isulin when needed
• To adjust the insulin amount in order to keep the
  glucose and insulin concentrations as close as
  possible to the concentrations of a normal person
  

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System architecture

• Three groups of agents working in an asynchronous
  way knowledge extraction, decision making, and
  plan generation
• Several types of decisional agents with only
  partial views of the phenomenon to be controlled
  and different viewpoints (eg physiological
  models)
• Presence of overlapping decisional models the
  input parameters as well as the output proposed
  decisions may intersect
• A negotiation mechanism to fuse the corresponding
  decisions 

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Agent s role

• Knowledge extraction agent extract high-level
  information (parameter values) from low-level
  data received from sensors 
• Decisional agents generate a set of decisions
  in terms of desirable new states ( corrected 
  values for the parameters to be monitored)
• Actuator agents generate the sequence of
  actions to perform to reach the desired states
• The agents communication is mediated by two
  dedicated blackboards the parameter blackboard
  and the knowledge blackboard

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System architecture
Decision Making
Knowledge Extraction
Plan Generation



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Extractor agents

• An extractor agent
• is connected to sensors,
• from which it acquires signal information,
• that it filters and processes,
• to generate the values of a set of parameters
• The parameters values generated by all the
  extractor agents are placed in the parameters
  blackboard.

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Implemented extractor agent

• The implemented extractor agent puts in the
  parameters blackboard a vector of parameter
  describing
• The current level of insulin
• The current level of glucose
• The current variation of the glucose
• The current level of the physical activity (as
  provided by piezoelectric crystal sensor for
  example)

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Decisional agents

• The decisional agents
• Read the parameter information from the
  parameters blackboard 
• Computes a  decision  as a pair (desired target
  value for a parameter, weight)
• The computation of the desirable target value is
  based on the agent internal model, on the current
  values of parameters, and on the effects of past
  decisions
• The weight is a measure of how much the current
  parameter value is away from optimum and, thus,
  of how much the decisional agent  wants  to
  reach the proposed target value for that
  parameter
• Put the result (p,w) in the knowledge blackboard

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Decisional agent s model

• Each decisional agent embeds the model of a
  particular physiological process aspect
• This model must provide a measure of how far the
  patient s state is from the optimum
• The model must also account for the
  interdependencies between the patient s
  physiological state, pathological state and
  activity
• Given a model, a set of desirable target states
  that minimize the distance to the optimum
  ( potential  values) is computed by means of an
  heuristic gradient descent technique

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Decisional agent s model

• Samples of the evolution over time of the levels
  of insulin, glucose and glucose variations are
  collected, given a pathology level (in terms of
  the insulin basal secretion level and glucose
  variation sensibility), a food absorbtion curve
  and a physical activity level
• These curves are then sampled, thus providing the
  parameter values corresponding to different
  pathological states, in different life conditions
  these values are the indexes of the matrix
• The matrix values (  potential  values), are
  computed for each set of indexes as the distance
  between the corresponding pathological parameter
  and the one of a normal person smaller values
  correspond to more desirable states

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Decisional agents

• Every parameter value of a proposed target point
  has an associated weight, which is the product of
  two factors
• the difference between the potential of the
  current value and the potential of the proposed
  value
• a weight measuring the  importance  of the
  physiological function controlled by the
  decisional agent
• for example by setting the importance weights it
  is possible to give higher priority to the
  control of vital functions than to the control of
  peripheral functions

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The implemented decisional agents (1)

• The first decisional agent embeds a simplified
  control model of the glucose-insulin metabolism
  related to food adsorption
• Its role is to compute desired values and weights
  for the level of insulin, level of glucose and
  variation of the glucose, based on their current
  values
• This first decisional agent tries to reduce the
  glucose concentration during food adsorption

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The implemented decisional agents (2)

• The second decisional agent embeds a simplified
  control model of the glucose-insulin metabolism
  related to physical activity
• Its role is to compute desired values and weights
  for the level of insulin, level of glucose and
  variation of the glucose based on their current
  values and the level of activity
• This second decisional agent tries to keep
  constant the glucose level by limiting the
  exogenous insulin introduction when the physical
  activity is intense.

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Agent s importance

• The value of the importance is variable according
  to the current state of the system
• The importance of the first decisional agent,
  which is related to the food absorbtion, is
  higher than that of the second decisional agent,
  which is related to physical activity
• This reflects the fact that the main problem of a
  diabetic patient is the insulin response when the
  patient eats

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The negotiation mechanism

• Different decisional agents can propose different
  variations for the same parameters, generating
  conflicts
• For example, the first decisional agent may
  propose to decrease the glucose level after a
  meal whilst the second may propose to keep
  constant the glucose level because the patient is
  undergoing an intense physical activity
• In the proposed approach, the relations between
  agent s models are not explicitely considered
  and the decisional agents are not aware of the
  presence of the others
• Necessity of an external negotiation mechanism
  (the equalizer, situated in the knowledge
  blackboard)

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The negotiation mechanism

• To make a final decision, the decisional agent
  has to select a single target state
• For this purpose, it calculates the cost of the
  variation of each parameter from the current
  state to a target state, as the weighted sum of
  the measure variations
• The weights are computed as the sum of two
  elementary costs 
• The actuation cost ( physical  cost)
• The negotiation cost ( social  cost)

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The negotiation mechanism

• The unitary cost of the parameter is the sum of
  two elements
• The actuation cost is determined by the actuator
  agent that acts on the parameter it measures
  the cost of the physical variation of the
  parameter
• The negotiation cost is determined by the
  equalizer component during the negotiation
  process it measures the difficulty to change
  the parameter according to the desires of the
  other decisional agents

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The equalizer component

• The role of the equalizer component is to
• Collect the decisions of the decisional agents
• Calculate for each parameter a new value as the
  weighted average of the proposed target values
  for that parameter
• For each agent determine and send the cost of its
  decision as its distance to the mean
• This cost is an approximation of the difficulty
  for a given agent to reach its proposed target
  value given the other proposals from the other
  decisional agents
• High values mean that the decisional agent is not
  in accordance with the other decisional agents
  about the considered parameter.

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The equalizer component

• The equalizer performs the described operations
  for each parameter a number of times before the
  end of a given time interval
• Thus during this negotiation time, the target
  states proposed by decisional agents are changed
  according to new negotiation costs
• Since the agents are asynchronous, the
  frequencies of parameters readings (by decisional
  agents) and of decision making (by the equalizer
  component) may be different

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Decision as negotiation

• The final result represents a state that is not
  the optimum for every decisional agent (i.e., for
  every physiological model), but it is somehow the
  optimum for social welfare of the system
• The decisions of some decisional agents (for
  instance the ones with lower intrinsic
  importance) are sacrificed for the global
  goodness of the system

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Actuator agent

• The actuator agent
• Receives parameter information from the equalizer
  each time a new set of values for these
  parameters is computed 
• Generates a sequence of operations to move them
  toward a target value
• Calculates the actuation costs and sends them to
  the decisional agents
• Executes the plan by exploiting the actuators
• The implemented actuator agent plans the
  variation of insulin to accomplish at a given
  time

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The negotiation mechanism at a glance
Decision Agent 1
Physical cost
Social cost
Agent decision
Actuator Agent 1
Equalizer
Target state
Agent decision
Social cost
Physical cost
Decision Agent 2
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effect of the negotiation mechanism

• The diabetic patient is underdoing an intense
  physical activity
• (a) Only the decisional agent that regulates the
  glucose-insulin meta-bolism with respect to food
  absorption is activated
• (b) Insertion of the second deci-sional agent,
  which is specifically devoted to regulate the
  glucose-insulin metabolism with respect to
  physical activity

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effect of the negotiation mechanism

• (c) The two decisional agents have the same
  importance From the oscillations of the insulin
  curve it appears the contrasting action of the
  two decisional agents that could not reach an
  agreement on the insulin value.
• Moreover, the insulin curve is similar to that
  obtained with a single decisional agent, with the
  difference that the oscillations are around lower
  insulin values because of the action of the
  second decisional agent.

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Discussion

• Quite seldom a physiological process can be
  modelled by a complete description from which to
  derive system design
• The proposed approach allows to cope with the
  co-existence of multiple partial models of a
  given phenomenon
• These models may overlap and result in
  conflicting decisions 
• The approach further allows to investigate the
  interdependencies among models that control
  related phenomena
• Rather than obeying a predefined global plan, a
  control system for the global phenomenon simply
  emerges from the structured interaction of the
  partial controller units

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Artificial Intelligence in Medicine Journal
Volume 27, Issue 3, Pages 229-400 (March 2003)

• J.Vázquez-Salceda, J.A.Padget, U.Cortés,
  A.López-Navidad F.Caballero Formalizing an
  electronic institution for the distribution of
  human tissues
• L.Godo, J.Puyol-Gruart, J.Sabater, V.Torra,
  P.Bar-rufet X.Fàbregas A multi-agent system
  approach for monitoring the prescription of
  restricted use antibiotics
• Y.Xu, D.Sauquet, P.Degoulet M.C.Jaulent
  Com-ponent-based mediation services for the
  integration of medical applications
• F.Amigoni, M.Dini, N.Gatti M.Somalvico
  Anthropic agency a multiagent system for
  physiological processes
• R.M.Vicari, C.D.Flores, A.M.Silvestre,
  L.J.Seixas, M.Ladeira H.Coelho A multi-agent
  intelligent envi-ronment for medical knowledge
• T.Alsinet, C.Ansótegui, R.Béjar, C.Fernández and
  F.Manyà Automated monitoring of medical
  protocols a secure and distributed architecture

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Further readings

• Amigoni, F. Dini, M. Gatti, N. Somalvico, M.
  (2003). Anthropic Agency A Multiagent System for
  Physiological Processes. Artificial Intelligence
  in Medicine, Elsevier, Vol. 27, n3, pp.305-334.
• Bloch, I. (1996, Information combination
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Further readings

• Hoc, J.M. (1996), Supervision et contrôle de
  processus  la cognition en situation dynamique,
  Grenoble  PUG.
• Hutchins E. (1995), Cognition in the wild, MIT
  Press.
• Ferber, J. (1999), Multi-Agent System An
  Introduction to Distributed Artificial
  Intelligence, Harlow Addison Wesley Longman
• Miksch, S., Cheng, K., Hayes-Roth, B. (1996) The
  Patient Advocate A Cooperative Agent to Support
  Patient-Centered Needs and Demands, in Cimino, J.
  J. (ed.), Proceedings of the 1996 AMIA Annual
  Fall Symposium (formerly SCAMC), Hanley Belfus,
  Philadelphia, pp. 144-148.
• Minsky, M. (1985),The Society of Mind. Simon
  Schuster, New York, USA.
• Santini, S. (2002), Image Retrieval, IEEE
  Intelligent Systems, vol.17, n 1, pp.79-81.
• Vygotsky, L.S. (1978), Mind in Society. The
  Development of Higher Psy-chological Processes.
  M. Cole, V. JohnSteiner, S. Scribner E.
  Souberman (eds), Harvard Univ. Press. Cambridge,
  Massachusetts.