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Automated Medicare decision support system

Automated Medicare decision support system. By Ahmed Atyya Ali Radwa Saeed Ammar Rana Samy Hammady Salsabeel Mouhamed Meriam Mouhamed. Supervised By Dr. Islam T.El Kabani. Why Medicare DSS?. We are trying to solve the problem of. Movie. The Flow of the System. The Mai n Server.

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Automated Medicare decision support system

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  1. Automated Medicare decision support system

  2. By Ahmed Atyya Ali RadwaSaeedAmmar RanaSamyHammady SalsabeelMouhamed MeriamMouhamed Supervised By Dr. Islam T.ElKabani

  3. Why Medicare DSS?

  4. We are trying to solve the problem of

  5. Movie

  6. The Flow of the System

  7. The • Main Server The server by its role gets the history and analysis of the ID from the database First the client send the symptoms and signs for some patient’s ID to the Server Now the data needed for diagnosis are ready to be sent to the Expert System And from the Server back to the client with the Diagnosis The Expert System The Expert System sends back the Diagnosis to the server Dataware- house Needed data ID Information Diagnosis client

  8. The • Main Server The Expert System Dataware- house Needed data ID Information Diagnosis client

  9. Datawarehouse ETL Data entry client ID Information

  10. Tools used DBMS : My SQL to make operational database and data warehouse . Java for the entry client (GUI represent clients and transactions )

  11. Data, Information, Knowledge • Data • Facts without meaning • Information • Organized data that has meaning and value • Knowledge • It’s the relation between data and information that use for information deduction.

  12. Operational database • Current value data such as analysis data • Can be updated • Need normalization • It’s components • Data • Hardware • Software • Users

  13. Data warehouse • Collection of data in support of management’s decision-making • Doesn’tneed normalization • Non volatile • Time variant • Subject oriented

  14. other sources Extract Transform Load Refresh Operational DBs Multi-Tiered Architecture Monitor & Integrator Server Metadata Expert system Serve Data Warehouse Data Marts Data Sources Data Storage Engine Front-End Tools

  15. 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

  16. The • Main Server The Expert System Dataware- house Needed data ID Information Diagnosis client

  17. The • Main Server Tool Used Java The traffic organizer Needed data ID Information Diagnosis client client client client

  18. 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?

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

  20. Multithreading Is the capability of running multiple tasks concurrently within a program Thread Synchronization A shared resource may be corrupted if it is accessed simultaneously by multiple threads The synchronized keyword To avoid race conditions

  21. The • Main Server Why is synchronization important in the Medicare System? client client client client

  22. The user interface

  23. 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

  24. The • Main Server The Expert System Dataware- house Needed data ID Information Diagnosis client

  25. TheExpertSystem Needed data The brain of the system Diagnosis Needed data

  26. An Expert System is a program that behaves like an expert for some problem domain. Ours posses as a physician advisor • It should be capable of explaining its decisions and the underlying reasoning.

  27. Often an Expert System is expected to be able to deal with uncertain and incomplete information. Some times the diagnosis is uncertain • They are also called knowledge-based systems • as they should posses knowledge in some form The set of diseases the expert system deal with

  28. Main structure of an expert system • Knowledge base: • Comprises the knowledge that is specific to the domain of the application • As simple facts about the domain • Rules that describe relations or phenomenon in the domain • Ideas of solving problems in this domain An inference engine: Knows how to actively use the knowledge in the base A user interface: Caters for smooth communication between the user and the system

  29. Structure of the Expert System. Knowledge base Inferenceengine Userinterface User

  30. Modularity Each rule defines a small relatively independent piece of knowledge Incrementability New 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 transparency The system’s ability to explain its decisions and solutions

  31. Backward chaining • 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

  32. Forward 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 driven • And that’s why ours is forward chaining

  33. Introducing uncertainty • Information about the problem to be solved can be incomplete or unreliable • Relations in the problem domain can be approximate • The representation assumes problem domains that answers all questions by either true or false, not somewhere between

  34. 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.

  35. 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 • Intelligence • Design • Choice • Implementation

  36. Decision-Making Intelligence Phase • Scan the environment • Analyze organizational goals • Collect data • Identify problem • Categorize problem Programmed and non-programmed Decomposed into smaller parts Assess ownership and responsibility for problem resolution

  37. Decision Support Systems Intelligence Phase • Automatic Data Mining Expert systems, CRM, neural networks • Manual OLAP KMS • Reporting Routine and ad hoc

  38. 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

  39. 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

  40. Implementation Phase • Improved communications • Collaboration • Training • Supported by KMS, expert systems, EIS, GSS

  41. 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

  42. Components of DSS -DSS Subsystems • Data management Managed by DBMS • Model management Managed by MBMS • User interface • Knowledge Management andorganizational • knowledge base

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