Deck 22: Data Mining As a Research Tool

Question 1

Reflect on these terms: database, data warehouse, and data mining. What do they have in common? How do they differ?

Answer 1

No Answer

Question 2

Describe an issue associated with healthcare data that impedes the construction of meaningful databases and inhibits the data mining process. Which strategies would you use to remedy this situation? Thoroughly describe one strategy and its potential outcomes.

Answer 2

No Answer

Question 3

Suggest a data mining project for your practice. Which information would you like to have about your practice area that could be extracted using data mining strategies?

Answer 3

No Answer

Question 4

Data mining is associated with numerous techniques and algorithms. How can you make sure that you select and develop those that best fit your data?

Question 5

The term big data refers to the process of using software to sort through data to discover patterns and ascertain or establish relationships.

Question 6

Exploration begins with exploring and preparing the data for the data mining process.

Question 7

The term knowledge deployment refers to taking the pattern and model identified in the pattern discovery phase and applying them to new data to test whether they can achieve the desired outcome.

Question 8

Health care does not generate big data.

Question 9

Bagging is a term for the use of voting and averaging in predictive data mining to synthesize the predictions from many models or methods or the use of the same type of a model on different data.

Question 10

The term boosting refers to increasing the power of the models generated by weighting the combinations of predictions from those models into a predicted classification.

Question 11

Exploratory data analysis is an approach or philosophy that uses mainly graphical techniques to gain insight into a dataset.

Question 12

Meta-learning is a subset of artificial intelligence that permits computers to learn either inductively or deductively. Inductive machine learning is the process of reasoning and making generalizations or extracting patterns and rules from huge datasets.

Question 13

Synthesizing predicted classifications to generate a final best predicted classification is a process referred to as stacking.

Question 14

Neural networks represent nonlinear predictive models.

Question 15

Algorithms are typically computer-based recipes or methods with which data mining models are developed.

Question 16

Brushing is a technique in which the user manually chooses specific data points or observations or subsets of data on an interactive data display.

Question 17

The acronym SEMMA-sample, explore, modify, model, assess-refers to the core process of conducting data mining.

Question 18

A nurse is preparing data for the data mining process and is identifying important variables. What phase of data mining is this?

A) Exploration
B) Problem identification
C) Knowledge deployment
D) Pattern discovery

Question 19

A nurse researcher analyzes databases to determine patterns of disease. Discovery of this knowledge will assist with which of the following?

A) Prevention of sequelae of the disease
B) Generation of new treatments
C) Standardization of nursing care plans
D) Prevention of disease

Question 20

A nurse researcher understands that data mining is most successful to reduce inefficiencies, improve quality, and support transformations when paired with which of the following?

A) Interprofessional collaboration
B) Nursing research
C) Data warehousing
D) Quantitative research

Question 21

A nurse researcher clusters data so that the large datasets are broken up into more manageable, smaller datasets. What is this process known as?

A) Data reduction
B) Drill down
C) Boosting
D) Bagging

Question 22

A nurse researcher is using a data mining model that uses the steps of sample, explore, modify, model, assess. Which model is the nurse researcher using?

A) SEMMA model
B) Six Sigma model
C) KDD model
D) Informatics model

Question 23

A nurse researcher is using the electronic health record (EHR) to identify patients who fall into gaps of care. What function embedded in the EHR assists the nurse researcher in identifying these patients?

A) A registry
B) A database
C) A quality alert
D) A diagnosis code

Question 24

A nurse researcher is using aggregate patient data for data mining. How is patient confidentiality maintained?

A) By deidentifying patient data
B) By locking data in a secure location
C) By storing data in a data warehouse
D) By using electronic data collection only

Question 25

A nurse researcher is using descriptive statistics to complete a fast analysis of shared data. What is this process known as?

A) Online analytic processing
B) An electronic algorithm
C) A decision tree analysis
D) A neural network

Question 26

Data mining looks at data from different perspectives and brings new insights to the dataset.

Question 27

The decision tree mining technique uses regression to predict the outcome of y.

Question 28

A nurse is beginning the analytical and logical process to forecast a pattern from data that has been concealed. What is this process called?

A) Data mining
B) Data retrieval
C) Data discovery
D) Data collection

Question 29

A nurse researcher is teaching a nursing student about data mining and nursing research. Which statement by the nurse researcher is correct?

A) "Nursing research will help transform big data into comprehensible knowledge."
B) "Nursing research will help analyze the outcomes prior to data collection."
C) "Nursing research will identify qualitative relationships for quantitative data."
D) "Nursing research will take nursing knowledge and transform it into big data."

Question 30

A nurse researcher is averaging predictive data mining to synthesize the predictions. The predictions arise from which of the following?

A) Different methods and models
B) Complex, large datasets
C) Use of different classifiers
D) Data assumptions

Question 31

A nurse researcher is starting a drill-down analysis and begins with which of the following?

A) Identifying variables of interest
B) Identifying graphical techniques to gain insight into a dataset
C) Making a generalization about a large dataset
D) Identifying a complex model to mine a dataset

Question 32

A nurse researcher starts to assimilate data. Once the nurse starts to understand the data, which of the following occurs?

A) Data quality issues are identified.
B) Data is processed into a model.
C) Study conclusions are made.
D) A new data mining project is started.

Question 33

A nurse practitioner is using a registry in the electronic health record to identify patients at risk for diabetes. Which data can the registry identify to assist with this data mining for diabetes?

A) Patient glucose laboratory results
B) Patient contact logs
C) Patient medical histories
D) Patient vital signs

Question 34

A nurse researcher is explaining neural networks for data mining to a nursing student. Which statement by the nurse researcher is correct?

A) "Neural network are linear, predictive models learn through training."
B) "Neural networks discover previously unknown relationships in large datasets in nonlinear modeling."
C) "Neural network models use a stacking method for data mining."
D) "Neural networks combine the predictions from several models."

Question 35

A nurse researcher is using a complex model to mine a small dataset of information. This is known as which of the following?

A) Bagging
B) Boosting
C) Exploratory data analysis
D) Data drill down

Question 36

To develop a successful data mining process, organizations must first have the data needed to create meaningful information.

Question 37

Decision trees are a statistical technique based on using numerous algorithms to predict an independent variable.