By
Akshay Dalvi
Bimal Paul
Vikrant Choudhary
What Is Cybernetics?
 Cybernetics began as the science of communication
and control in the animal, machine, and society; i.e.
special types of systems. It operates on two levels:
study of an observed system & study of the people
studying a system. Originated from R & D process in
the development of the atomic bomb- applied
scientific theory & principles in real-world setting.
What Is Medical Cybernetics?
• Application of systems and
communications theory, connectionism
and decision theory on biomedical
research and health related questions
• Investigates intercausal networks in
human biology, medical decision making
and information processing structures in
the living organism
What is it used for?
• Physiology and Pathology
o
Diagnosis, therapy, and prevention of diseases
• Medical Informatics
o
Deals with resources and methods required to optimize the
transfer and use of information in health and biomedicine.
 Applied to areas of nursing, clinical care, dentistry,
pharmacy, public health and (bio)medical research.
Applications in Medical Field
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Admissions, Discharges, and Transfers
Medication Order Entry
IV Order Entry
Common Order Entry
Bar Code Support
Physician Order Entry, Profile Review and Management
Unit Dose Support
It allows the nurse access to review and print the patient profile, generate MARs at
the nursing station, as well as physician order updates, discharge order sheets, and
multiple nursing worksheets.
Clinical Screening for Drug Interactions, Drug Allergies, Food/Drug interactions,
Dosing, and Therapeutic Duplications.
Drug Utilization Review
Diagnostic Related Grouping (DRG) Statistics and Reporting
Inventory Control
Statistical Reports and Lists
Outpatient Pharmacy Module
File Generation and Maintenance
Topics In Medical Cybernetics
• Systems Theory in Medical Sciences
Involves searching for and modeling of physiological dynamics
in a organism to gain deeper insights into the organizational
principles of life and its disturbance
• Medical Information and Communication Theory
o Aims to mathematically describe signaling process and
information storage in different physiological layers
• Connectionism
o Aims to describe information processing in neural networks
• Medical Decision Theory (MDT)
o Aims to gather evidence based of foundations for decision
making in the clinical setting
o
A System Theory Approach to an expanded
medical model
Fig:
Multidimensional Healing: The Clinical Process.
What is Medical Information and Communication Theory?
Medical Information and Communication Theory in Medical
Cybernetics mathematically describes the signal transfer processes in
different physiological layers.
Basics
- Information typically managed through a combination of cognitive
memory and paper based systems
- Technology comes along, aides in tedious tasks such as financing
- Computing allows for complex communications
- Significantly changes the attitude of communication.
- Two forms of communication, horizontal and vertical
-Horizontal-> at the process level, linking activities together, supporting
front line decision makers, and enabling efficient flow of business
-Vertical-> management between top level and the intermediate levels
of computing.
What Is Connectionism?
• Connectionism is a set of approaches that models mental or
behavioral phenomena as the emergent process of interconnected
networks of simple units.
o Most commonly used model of connectionism is the neural
network.
• The central principle of connectionism is that mental development
can be described by interconnected networks of simple units.
However the form of connections and units can vary from model
to model.
Neural Networks
 A Neural network is a massively distributed processor
that has a natural propensity for storing experimental
knowledge and making it available for use. It
resembles the human brain in 2 respects
 Knowledge acquired though a learning process
 Interneuron connection strengths known as synaptic
weights store knowledge.
Biological Neuron
Artificial Neuron
 Transfer Function
 Weighted summation
 Activation Function
 Threshold Function


OUT=1
OUT=0
if NET>T
otherwise
 Squashing Function
 OUT=1/(1+
)
e
 NET
 Hyperbolic Tangent Function
 OUT=tanh(NET)
Recurrent Neural Network
Training
 Objective- Application of a set of inputs produces
desired(or at least consistent) set of outputs.
 Types
1. Supervised Training
2. Unsupervised Training
3. Reinforcement Training
Neural Networks + WSN in the
medical field
 What exists?
 What can be done?
What Is Decision Theory?
• Aims to identify various
issues relevant in a decision and
its rationality.
•
Medical uses include:
o
Medical Diagnosis

o
Clinical Decision Support
Systems (CDSS)
Health Informatics

Deals with the resources required
to optimize the retrieval and use
of information in health and
biomedicine
Clinical Decision Support Systems
• Computer programs designed to assist physicians and other
health professionals with decision making tasks.
• Two main types:
Knowledge Based
 Make use of huge database and decision trees.
o Non-knowledge based
 Commonly use neural networks or genetic algorithms to
find patterns in clinical data.
o
APPLICATION
 WIRELESS BIOMEDICAL SENSORS
 CODE BLUE : HARVARD UNIVERSITY
CodeBlue
 Harvard University in collaboration with various
medical facilities introduce CodeBlue
 CodeBlue - An ad hoc WSN Infrastructure for
Emergency Medical Care
 Goal – “Enhance first-responders’ ability to access
patients on scene, ensure seamless transfer of data
among caregivers, and facilitate efficient allocation of
hospital resources”
CodeBlue: VitalDust
 Wearable wireless pulse oximeter and 2-lead
Electrocardiogram Monitor (EKG)
 Collect heart rate (HR), blood oxygen saturation (SpO2),
and hearts electrical activity
 Devices can be programmed to alert medical personnel
when vital signs fall outside normal conditions
CodeBlue: VitalDust Implementation
 Pulse Oximeter
 Mote-based oximeter
connector between
Mica2/MicaZ mote
platform and the
BCI Medical board
 Measures the amount of
light transmitted through a
noninvasive sensor attached
to the patient’s finger
CodeBlue: VitalDust Implementation
 EKG
 Mote-based EKG consists
of a custom built circuit
board attached to a
Mica2/MicaZ/Telos mote
 Measures hearts’
electrical activity
through a set of leads
attached to a patients
heart at a rate of 120 Hz
CodeBlue: Pluto
 Wearable tag wristband
 Stores patient information
and tracks patient location
using radio-frequency
(RF) signals
 Mote includes an external push button that can be
used by a patient to transmit a one-way alert to
medical staff
CodeBlue Infrastructure
Challenges
 Communication Challenges
 Secure, reliable, ad hoc communication among groups
of sensors and mobile devices
 Prioritize transmission of data
 Computational Challenges
 Computational power
 Security and encryption techniques
 Programming Challenges
 Level of software services
Conclusion
 Extremely beneficial in disaster response scenarios
 Real Time Monitoring
 Requires efficiency and accuracy improvement
 A step up in saving lives, creating valuable medical
research data, and allocation of medical resources
References
•
http://www.medical-cybernetics.de/definition.html (3/7/2010)
•
http://en.wikipedia.org/wiki/Medical_cybernetics (3/7/2010)
•
S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach, 2nd ed. New
Jersey: Prentice Hall, 2003. pp. 724-748.
•
A. Hart. “Using Neural Networks for Classification Tasks – Some experiments on
Datasets and Practical Advice,” The Journal of Operational Research Society, vol. 43,
no. 3, pp. 215-226, Mar. 1992.
•
Raul Rojas. Neural Networks – A Systematic Introduction, NY, 1996. pp. 337-374
References Continued
•
M. Stensmo and T.J. Sejnowski. "Automated Medical Diagnosis based
on Decision Theory and Learning from Cases," World Congress on
Neural Networks 1996 International Neural Network Society, pp.12271231.
•
http://en.wikipedia.org/wiki/Clinical_decision_support_system
(03/14/2010)
•
Code Blue: An ad hoc sensor network infrastructure for emergency
medical care
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.62.5465