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Data warehousing and Data mining – an overview

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  1. Data warehousing and Data mining – an overview Dr. Suman Bhusan Bhattacharyya MBBS, ADHA, MBA

  2. Local Electronic Medical Record Capturing Clinical Data RDBMS Alerts & Warnings RDBMS RDBMS In house Regional • Displaying data • Displaying rule-based patient-specific alerts • Displaying pre-set warnings • Following clinical protocols • Online Transactional Processing (OLTP) Today we have…

  3. Requirements of tomorrow • Use clinical data to • Support Evidence based medicine • Perform Outcomes Analysis • Confirm existing clinical “facts” • Refine clinical guidelines/protocols • Find hidden knowledge patterns

  4. Necessity of these requirements • Evaluation of stored data may lead to discovery of trends and patterns that would enhance the understanding of disease progression and management • Insurance companies of the future will clinically assess a person for the most likely risks for a specified period and then calculate the premium for health insurance

  5. Doing it right… Operational EMR Databases ExtractTransformLoadValidate [ETLV] External EMR Sources Data marts Data warehouse Metadata Repository Monitoring Administration Output OLAP Server OLAP Server

  6. Query/Report Data mining Analysis Output

  7. Knowledge Evaluation & Presentation Cleaning & Integration Databases Flat Files The way to go… Data mining Selection &Transformation

  8. Informatica Cognos Business Objects SPSS SAS tools Epi Info with Epi Report Custom-built Type of Commercial packages

  9. Thank Q