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  1. 1. Data Mining Applications In Healthcare 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  2. 2. Introduction Goals of today’s presentation: Provide an overview of the technologies that are relevant to the development and deployment of data mining solutions in healthcare Allow participants to evaluate where the technology is useful 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  3. 3. What is Divining knowledge Data mining? from data 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  4. 4. . Topic Outline Data mining • Uses • Algorithms • Technology • Applications in healthcare 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  5. 5. . Data Mining Uses • Descriptive Understand and characterize Clustering Summarization Association Rules Sequence Discovery • Predictive Extrapolate and forecast Classification Regression Time-Series 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  6. 6. Data Mining Algorithms • Classification > Statistical > K-nearest neighbors > Decision trees ▲ ▲ ID3 C4.5 > Neural Networks (Self Organizing Maps) • Clustering > Hierarchical > Partitioned > Genetic • Association > Apriori Algorithm > If….Then rules 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  7. 7. Technology solutions Technology Data Mining Infrastructure Technologies • Database Technologies • On-Line Analytical Processing (OLAP) • Visualization Technologies • Data scrubbing technologies • Natural Language Processing (NLP) 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  8. 8. Database Technologies •Database •OLAP • Data warehouse vs. Data mart •Visualization • Relational technologies > Oracle > Microsoft •Scrubbing •NLP • XML-databases > Raining Data 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  9. 9. On-Line Analytical Processing •Database •OLAP •Visualization • Analyze multi-dimensional data •Scrubbing • N-dimensional data cubes •NLP • Operations > Roll-up > Drill-down > Slice and dice > Pivot 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  10. 10. Visualization •Database •OLAP • 2D/3D Charts •Visualization • Topographic displays •Scrubbing • Cluster displays •NLP • Histograms • Scatter plots • Advanced visualization (genomic data patterns) • http://www.ncbi.nlm.nih.gov/Tools/ 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  11. 11. •Database •OLAP •Visualization •Scrubbing •NLP • Data cleansing • Filling in missing data • In healthcare, there is a strong need for deidentification to protect privacy 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  12. 12. De-Identification of Medical Records * • Names; • social security numbers; • all elements of a street address, city, county, precinct, zip code, & their equivalent • medical record numbers; • health plan beneficiary numbers; geocodes, except for the initial three digits of a zip code for areas that contain over 20,000 people; • account numbers; • certificate/license numbers; all elements of dates (except year) for dates directly related to the individual, (e.g., birth date, admission/discharge dates, date of death); and all ages over 89 • license plate numbers, vehicle identifiers and serial numbers; • device identifiers and serial numbers; and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older; • URL addresses; • Internet Protocol (IP) address numbers; • biometric identifiers, including finger and voice prints; • • • • telephone numbers; • fax numbers; • • full face photographic images and comparable images; e-mail addresses; • any other unique identifying number except as created by IHS to re-identify information. * Source: Policy and Procedures for De-Identification of Protected Health Information and Subsequent Re-Identification 45 CFR 164.514(a)-(c) posted by IHS (Indian Health Services) 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  13. 13. Natural Language Processing •Database •OLAP •Visualization •Scrubbing •NLP • NLP Uses > translation, summarization, information extraction, document retrieval or categorization • NLP Companies in health care > A-Life > Language and Computing • NLP Approaches > Clustering, Classification, Linguistic analysis, knowledge-based analysis 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  14. 14. Applications in Healthcare • Safety and quality • Clinical Research • Financial • Public Health 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  15. 15. “To err is Human” IOM Report •Safety and Quality •Clinical Research •Financial •Public Health • Characterization > JCAHO Core Measures > CMS Quality measures starter set > Improves patient care – reactive response • Prediction > Identifying cases that can result in bad clinical outcomes and raising appropriate alarms > Impacts patient care – proactive response 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  16. 16. Quality Measures – Initial Set* Starter Set of 10 Hospital Quality Measures Measure Aspirin at arrival Condition Acute Myocardial Infarction (AMI)/Heart attack Aspirin at discharge Beta-Blocker at arrival Beta-Blocker at discharge ACE Inhibitor for left ventricular systolic dysfunction Left ventricular function assessment Heart Failure ACE inhibitor for left ventricular systolic dysfunction Initial antibiotic timing Pneumonia Pneumococcal vaccination Oxygenation assessment *Source: http://www.cms.hhs.gov/quality/hospital/overview.pdf 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  17. 17. Safety and Quality • University of Mississippi Medical Center > Data Warehouse Technologies to understand Medication Errors – Funded by AHRQ > Anonymous report data collection > Data mining technologies > Use of Neural networks and associative rule inference 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  18. 18. Clinical Research & Clinical Trials •Safety and Quality •Clinical Research •Financial •Public Health • Pharmacy and medical claims data • Drug efficacy and clinical trials – for example how effective is a particular drug regimen • Protein structure analysis • Genomic data mining • Diagnostic Imaging data research 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  19. 19. The bottom line on cost •Safety and Quality •Clinical Research •Financial •Public Health • General Utilization review – does the care provided meet accepted clinical and cost guidelines • Drug Utilization review • Outlier analysis – exceptions to treatment – analyzing treatments which cost more than the normal or less than normal. 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  20. 20. Data mining in public health •Safety and Quality •Clinical Research • Syndromatic surveillance •Financial • Bio-terrorism detection •Public Health • Communicable disease reporting (Centers for Disease Control (CDC)) Example effort: AEGIS • DAWN (Drug Awareness and Warning Network) • Federal Drug Agency (FDA) – reporting of adverse drug events. 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  21. 21. Conclusion •Descriptive •Predictive •Classification •Clustering Data mining • Uses •Database •OLAP •Association rules •Visualization •Scrubbing • Algorithms •NLP •Safety and Quality • Technology •Clinical Research • Applications in healthcare •Financial •Public Health 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010
  22. 22. Technology solutions Conclusion uestions? 01010010010100100101001010101000101010101000101010010101010101010100101001001010100101010010010010001001001010010010000101010101001010101001001001001001010010101 01010010010010010100101010010010010010010010010010101000101000101001010010010010010101010010100100100100100100100100100100100100100101001010010010010010001010010

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