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Using Data Mining Strategies in Clinical Decision Making: A Literature Review

CIN: Computers, Informatics, Nursing: October 2016 - Volume 34 - Issue 10 - p 484
doi: 10.1097/01.NCN.0000504587.62271.53


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Back to Top | Article Outline


PURPOSE: To provide information about the use of data-mining strategies in clinical decision making.

LEARNING OBJECTIVES/OUTCOMES: After completing this continuing education activity, you should be able to:

1. Identify decision-making theories and data-mining models.

2. Discuss the use of data-mining strategies in clinical decision making.

  • 1. According to Paley et al and Bjork and Hamilton, the validity associated with decision making is analytical and logical as well as
    1. experiential.
    2. interpretive.
    3. c. linear.
  • 2. The process involved in the concept attainment theory of decision making is best described as
    1. circular.
    2. linear.
    3. c. triangular.
  • 3. Bayesian theory is also known as
    1. decision tree theory.
    2. probability theory.
    3. c. social judgment theory.
  • 4. Dual process theory combines rational theory with
    1. Bayesian theory.
    2. intuitive theory.
    3. c. social judgment theory.
  • 5. The last stage in the 7-stage theory of decision making is
    1. action.
    2. choice.
    3. c. feedback.
  • 6. Which data-mining model first calculates attributes into values, then computes the interconnection pattern between the different layers?
    1. cluster
    2. linear regression
    3. c. neural network
  • 7. With regard to decision making, data mining has been developed as a way to
    1. minimize errors.
    2. predict clinicians’ actions.
    3. c. provide hypotheses.
  • 8. The goal of data mining in clinical decision making is to recognize patterns and relationships in attributes of the clinical setting and
    1. provide direction.
    2. improve care.
    3. c. estimate the outcome.
  • 9. Which data-mining model did Worachartcheewan use to identify a metabolic syndrome?
    1. Bayesian
    2. cluster
    3. c. decision tree
  • 10. Which model uses fever and PO2/FIO2 ratio variables to predict the probability of an ICU patient with pneumonia?
    1. Bayesian
    2. cluster
    3. c. linear regression
  • 11. What Web-based data visualization tool generates reports, updates data sets, imports rules, and loads rules?
    1. the Clinical Explorer
    2. the Data Explorer
    3. the Expert Explorer
  • 12. Bowles et al compared decisions made by a human expert with those of a data-mining expert model and found that the data-mining expert model produced an accuracy of
    1. 77.6%.
    2. 87.6%.
    3. c. 97.6%.
  • 13. An advantage of using data mining is decreasing the time needed for
    1. making decisions.
    2. performing assessments.
    3. researching differential diagnoses.
  • 14. According to Wagholikar, using data-mining strategies in clinical decision making can be accurate, especially when
    1. assessing.
    2. diagnosing.
    3. c. treating.
  • 15. One limitation of data mining is that
    1. patterns are defined using a small database.
    2. it detects some conditions, such as rotator cuff tear, with low accuracy.
    3. patterns are found, but explanations are seldom provided.
  • 16. One of the most beneficial issues of data mining, compared with clinical decision making, is its
    1. classification structure.
    2. feedback system.
    3. recall.
  • 17. Compared with humans, who have a “limited channel capacity,” data mining is restricted by the
    1. database.
    2. c. user.
    3. programmer.
  • 18. Clinical decision-making, data-mining strategies are designed for
    1. a group of people.
    2. c. institutions.
    3. individuals.
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