Health Informatics an Interprofessional Approach 1st Edition By Nelson Staggers
Health Informatics an Interprofessional Approach 1st Edition By Nelson Staggers
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Chapter 4: Knowledge Discovery, Data Mining, and Practice-Based Evidence
Complete Chapter Questions With Answers
Sample Questions Are Posted Below
MULTIPLE CHOICE
| a. | Root mean squared error (RMSE) |
| b. | Area under the receiver operating characteristic (ROC) curve |
| c. | Percentage of correctly classified cases |
| d. | Hosmer-Lemeshow statistic |
ANS: B
The area under the receiver operating characteristic (ROC) curve is the most important overall measure of classifier performance.
DIF: Cognitive Level: Remember REF: p. 62
| a. | Stratified sampling |
| b. | Conceptual analytic methods |
| c. | Selection of the optimal subset of variables/features for use in modeling |
| d. | Machine learning |
ANS: C
Knowledge discovery and data mining (KDDM) makes use of specialized analytic methods, characteristically machine learning, to identify patterns in a semi-automated fashion.
DIF: Cognitive Level: Remember REF: p. 56
| a. | machine learning, use of statistical methods, large amounts of data. |
| b. | a process wherein patients are selected for clinical trials. |
| c. | “fishing” for information in data. |
| d. | cleaning data for further analysis using other methods. |
ANS: A
Knowledge discovery and data mining (KDDM) is a process in which machine learning and statistical methods are applied to analyze large amounts of data.
DIF: Cognitive Level: Remember REF: p. 56
| a. | Large sample sizes can be obtained easily. |
| b. | Multiple sites are typically enrolled, resulting in greater generalizability. |
| c. | Study efficacy is greater. |
| d. | Multiple variables can be studied. |
ANS: C
Practice-based evidence (PBE) designs trade away the internal validity of RCTs for external validity. PBE designs have high external validity because they include virtually all patients with, or at risk for, the condition under study, as well as potential confounders that could alter treatment responses. PBE designs attempt to minimize threats to internal validity by trying to collect information on all patient variables – demographic, medical, nursing, functional, and socioeconomic – that might account for differences in outcome. By doing so, PBE designs minimize the need for compensating statistical techniques such as instrumental variables and propensity scoring to mitigate selection bias effects, unknown sources of variance, and threats to internal validity.
DIF: Cognitive Level: Understand REF: pp. 62-6
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