Your SlideShare is downloading. ×
Chapter 24
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Chapter 24


Published on

Published in: Health & Medicine, Technology

  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. Chapter 24 Data Mining: A Research Tool
  • 2. Objectives 1. Describe big data. 2. Assess knowledge discovery in data. 3. Explore data mining. 4. Compare data mining models.
  • 3. Data Mining • Iterative process • Explores and models big data • Identifies patterns • Provides meaningful insights
  • 4. Big Data IBM (2013) describes big data in a way that is easy to understand. Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data (p. 1).
  • 5. Data Mining Focus • Producing a solution that generates useful forecasting through a four phase process: – 1. Problem identification, – 2. Exploration of the data, – 3. Pattern discovery, and – 4. Knowledge deployment, application to new data to forecast or generate predictions.
  • 6. Data Mining Facilitates • Data exploration and resulting knowledge discovery fosters proactive, knowledge driven decision making
  • 7. Exploratory Data Analysis (EDA) • Sometimes known as model building or pattern identification • Pattern discovery is a complex phase of data mining • Yields a highly predictive, consistent pattern identifying model
  • 8. Data Mining Known as KDD • KDD is known as –knowledge discovery and data mining –knowledge discovery and data –knowledge discovery in databases
  • 9. KDD • Term knowledge discovery is key • Data mining looks at the data from different vantage points, aspects and perspectives • Brings new insights to the data set
  • 10. Data Mining Defined Process of finding correlations or patterns among the data.
  • 11. KDD and Research • Berger and Berger (2004) –nurse researchers are positioned to use data mining technologies to transform the repositories of big data into comprehensible knowledge that is useful for guiding nursing practice and facilitating interdisciplinary research.
  • 12. CART (classification and regression trees) • data mining method for analyzing outcomes and service use
  • 13. Data Mining Concepts • Bagging • Boosting • Data reduction • Drill down • EDA • Feature selection • Machine learning • Meta-learning • Predictive • Stacking
  • 14. Data Mining Techniques • Neural networks • Decision trees – Chi square automatic interaction detection (CHAID) • Rule induction • Algorithm • Nearest neighbor • Text mining • Online Analytic Processing (OLAP) • Brushing
  • 15. Data Mining Models • CRISP-DM – 6 steps: business understanding, data understanding, data preparation, modeling, evaluation and deployment • Six Sigma – DMAIC steps: define, measure, analyze, improve and control. • SEMMA – sample, explore, modify, model, assess
  • 16. Benefits of KDD • Enhance business aspects • Help to improve patient care
  • 17. Ethics of Data Mining • Dependent on the use of private health information (PHI) • Insure data is de-identified and confidentiality maintained • Follow changes and specific requirements for compliance with HIPAA laws
  • 18. References • Berger, A. M., & Berger, C. R. (2004). Data mining as a tool for research and knowledge development in nursing. Comput Inform Nurs, 22(3), 123-131. PubMed ID: 15520581 • DeGruy, K. B. (2000). Healthcare applications of knowledge discovery in databases. J Healthc Inf Manag, 14(2), 59-69. PubMed ID: 11066649 • Fernández-Llatas, C., Garcia-Gomez, J. M., Vicente, J., Naranjo, J. C., Robles, M., Benedi, J. M., & Traver, V. (2011). Behaviour patterns detection for persuasive design in Nursing Homes to help dementia patients. Conf Proc IEEE Eng Med Biol Soc, 2011, 6413-6417. PubMed ID: 22255806 • Goodwin, L., Saville, J., Jasion, B., Turner, B., Prather, J., Dobousek, T., & Egger, S. (1997). A collaborative international nursing informatics research project: predicting ARDS risk in critically ill patients. Stud Health Technol Inform, 46, 247-249. PubMed ID: 10175406
  • 19. References • Green, J., Paladugu, S., Shuyu, X., Stewart, B., Shyu, C., & Armer, J. (2013). Using temporal mining to examine the development of lymphedema in breast cancer survivors. Nurs Res, 62(2), 122-129. PubMed ID: 23458909 • IBM. (2013). Big data at the speed of business. Retrieved from http://www- • Lee, T., Lin K., Mills, M., & Kuo, Y. (2012). Factors related to the prevention and management of pressure ulcers. Comput Inform Nurs, 30(9), 489-495. PubMed ID: 22584879
  • 20. References • Lee, T., Lin K., Mills, M., & Kuo, Y. (2012). Factors related to the prevention and management of pressure ulcers. Comput Inform Nurs, 30(9), 489-495. PubMed ID: 22584879 • Lee, T., Liu, C., Kuo, Y., Mills, M., Fong, J., & Hung, C. (2011). Application of data mining to the identification of critical factors in patient falls using a web-based reporting system. Int J Med Inform, 80(2), 141-150. PubMed ID: 21115393 • Madigan, E. & Curet, O. (2006). A data mining approach in home healthcare: outcomes and service use. BMC Health Serv Res, 6, 18. PubMed ID: 16504115
  • 21. References • Manyika, J., Chu, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. (2011). McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity. Retrieved from technology/big_data_the_next_frontier_for_i nnovation
  • 22. References • SAS. (n.d.). SAS enterprise miner. Retrieved from tics/datamining/miner/semma.html • Tishgart, D. (2012). Why security matters for big data and health care: Data integrity requires good data security. Retrieved from • Trangenstein, P., Weiner, E., Gordon, J., & McNew, R. (2007). Data mining results from an electronic clinical log for nurse practitioner students. Stud Health Technol Inform, 2007; 129, 1387-1391. PubMed ID: 17911941 • Zupan, B. & Demsar, J. (2008). Open-source tools for data mining. Clin Lab Med, 28(1), 37-54. PubMed ID: 18194717