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Intelligent Systems - Coggle Diagram
Intelligent Systems
DSS
Data-Driven DSS
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E.g. Department of Education NSW: Tracks enrolment trends and demographic data to allocate resources
Model-Driven DSS
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Can include decision trees, optimisation models, simulation
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Knowledge-Driven DSS
Based on rules, facts, or expert logic
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E.g. Healthdirect Australia: Symptom checker suggests medical action based on user input and medical knowledge base
Document-Driven DSS
Retrieves and analyses textual, non-structure data
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useful in legal, medical, policy, and compliance decisions
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Communication-Driven DSS
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Tools include chat, conferencing, shared whiteboards, and group decision software
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Hybrid DSS
Many modern enterprise DSS are hybrid, combining multiple types
E.g.: An education budgeting tool might: - pull data from enrolments (Data-Driven), Use a simulation model to test funding scenarios (Model-Driven), Embed policy documents (Document-Driven), Allow school leaders to collaborate (Communication-Driven)
Common Applications
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Healthcare
clinical decision support systems help healthcare professionals diagnose diseases and recommend treatments
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Decision Type
Structured
routine, repetitive decisions with clear procedures; can be fully automated using algorithms and rules
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Semi-structured
Decisions with defined procedures, but which require some human judgement and interpretation
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Unstructured
Complex, novel decisions without clear rules or procedures; rely heavily on human judgement and experience
Features:
-High uncertainty
-No standardised procedures