Automated Medicare decision support system

ByAhmed Atyya Ali

Radwa Saeed Ammar

Rana Samy HammadySalsabeel

MouhamedMeriam Mouhamed 

Supervised ByDr. Islam T.El

Kabani

Why Medicare DSS?

We are trying to solve the

problem of

Movie

The Flow of the System

Diagnosis

Dataware- house

The Expert System

First the client send the

symptoms and signs for some patient’s ID to

the Server

The server by its role gets the

history and analysis of the

ID from the database

ID

Information

Needed data

Now the data needed for

diagnosis are ready to be sent

to the Expert System

The Expert System sends

back the Diagnosis to the server

And from the Server back to the client with the Diagnosis

The

Main Server

The

Main Server

client

Diagnosis

The Expert System

ID

Information

Needed data

The

Main Server

The

Main Server

client

Dataware- house

Datawarehouse

Information

ID

ETLData

entry client

Tools used

DBMS : My SQL to make operational database and data warehouse .

Java for the entry client (GUI represent clients and transactions )

Data, Information, Knowledge

DataFacts without meaning

InformationOrganized data that has meaning and value

KnowledgeIt’s the relation between data and information that use for information deduction.

Operational database

Current value data such as analysis data Can be updated Need normalizationIt’s components Data Hardware Software Users

Data warehouse Collection of data in support of

management’s decision-making

Doesn’t need normalization

Non volatile

Time variant

Subject oriented

Multi-Tiered Architecture

DataWarehouse

ExtractTransformLoadRefresh

Engine

Expert system

Monitor&

IntegratorMetadata

Data Sources Front-End Tools

Serve

Data Marts

Operational DBs

other

sources

Data Storage

Server

Data Warehouse Parts

1-The data warehouse itself, which contains the data and associated software

2-Data acquisition (back-end) software, which extracts data from legacy systems and external sources, consolidates and summarize them, and loads them into the data warehouse

3-Client (front-end) software, which allows users to access and analyze data in the warehouse

Diagnosis

Dataware- house

The Expert System

ID

Information

Needed data

The

Main Server

The

Main Server

client

Diagnosis

ID

Information

Needed data

The

Main Server

The

Main Server

client clientclient client

The traffic organizer

Tool UsedJava

The server is a computer program that provides services to other programs in the same or other computers.

What is the main role of the server in the system?

It is the main connectivity tool between the modules of the system

Multithreading

Is the capability of running multiple tasks concurrently within a program

Thread SynchronizationA shared resource may be corrupted if it is accessed simultaneously by multiple threadsThe synchronized keywordTo avoid race conditions

Why is synchronization important in the Medicare System?

The

Main Server

The

Main Server

client clientclient client

The user interface

The aggregate means by which the doctors interact with the system

It provides means of input which are the symptoms and the signs of patient

And means of output which is the diagnosis of the patient case

Diagnosis

Dataware- house

The Expert System

ID

Information

Needed data

The

Main Server

The

Main Server

client

Diagnosis

Needed data

Needed data

The Expert System

The brain of the system

An Expert System is a program that behaves like an expert for some problem domain.

It should be capable of explaining its decisions and the underlying reasoning.

Ours posses as a physician advisor

Often an Expert System is expected to be able to deal with uncertain and incomplete information.

They are also called knowledge-based systemsas they should posses knowledge in some form

Some times the diagnosis is uncertain

The set of diseases the expert system deal with

Main structure of an expert system

Knowledge base:Comprises the knowledge that is specific to the domain of the applicationAs simple facts about the domainRules that describe relations or phenomenon in the domainIdeas of solving problems in this domainAn inference engine:Knows how to actively use the knowledge in the baseA user interface:Caters for smooth communication between the user and the system

Structure of the Expert System.

Knowledge base

Inference engine

User interfac

eUser

ModularityEach rule defines a small relatively independent piece of knowledge

IncrementabilityNew rules can be added to the knowledge base relatively independently of other rules

Modifiability (as a consequence of modularity)

Old rules can be changed relatively independently of other rules

Support system transparencyThe system’s ability to explain its decisions and solutions

In backward chaining we start with a hypothesis and work backwards according to the rules in the knowledge base

It searches from goals to data, from diagnosis to findings ,etc.

That’s why we coal it goal driven

Backward chaining

Doesn’t start with the hypothesis, but with some confirmed findings

It starts with what is already known, derives all conclusions that follow from this and adds them to the fact relation

From data to goals, from findings to explanation or diagnoses, etc.

That’s why we call it data drivenAnd that’s why ours is forward chaining

Forward chaining

Introducing uncertainty

The representation assumes problem domains that answers all questions by either true or false, not somewhere between

Information about the problem to be solved can be incomplete or unreliable

Relations in the problem domain can be approximate

As we may not be quite sure that some symptom is present in the patient, or that some measurement data is absolutely correct.This requires probabilistic reasoning.

Decision Making

Process of choosing amongst alternative courses of action for the purpose of attaining a goal or goals.

The four phases of the decision process are

IntelligenceDesignChoiceImplementation

Decision-Making Intelligence Phase

Scan the environment

Analyze organizational goalsCollect dataIdentify problemCategorize problem Programmed and non-programmed Decomposed into smaller parts

Assess ownership and responsibility for problem resolution

Decision Support Systems

Intelligence Phase

AutomaticData MiningExpert systems, CRM, neural networks

ManualOLAPKMS

ReportingRoutine and ad hoc

Design Phase

Generation of alternatives by expert system

Relationship identification used in models through OLAP and data mining

Business process modeling using CRM, RMS, ERP, and SCM

Recognition of the problem through KMS

Choice Phase

Identification of best alternative

Identification of good enough alternative

What-if analysis

Goal-seeking analysis

May use KMS, GSS, CRM, ERP, and SCM systems

Implementation Phase

Improved communications

Collaboration

Training

Supported by KMS, expert systems, EIS, GSS

Database Management System

Extracts data

Manages data and their relationships

Updates (add, delete, edit, change)

Retrieves data (accesses it)

Queries and manipulates data (Query Facility)

Employs data dictionary

Components of DSS

- DSS SubsystemsData management

Managed by DBMSModel management

Managed by MBMSUser interfaceKnowledge Management and organizational knowledge base