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Grand Canyon University *
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Medicine
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Jan 9, 2024
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docx
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Uploaded by ConstableLion3405
As a data reviewer, it is important to determine that the data reliable and accurate. In clinical
research, it needs to be known what the source of where the data was extracted from and
examine the original files. In clinical research, that would be to look at the original results from
experiments conducted by the department. When compiling a report, the following will be
evaluated:
1) Are data results in compliance with Client method/protocol, Company's SOPs (Standard
operation procedure)/ policies?
2) Once compliant, are there any outliers? Is data missing?
3) Based on the missing data, is this considered reliable and accurate? Are there outliers?
4) Is this qualitative or quantitative analysis? What tests and how should the data be analyzed?
5) If numerical, is the data a normalized distribution? If non-numerical, how should the data be
analyzed?
6) Based on the goal, what criteria of the data is usable (accurate and reliable) to evaluate "cost-
saving opportunities"?
These steps listed above will only work based on the type of data and industry available.
However, this is not the only process available to do such. In a more basic concept, the data
needs to be process such that the data needs to be cleaned, integrated, selected, transformed,
observed, and evaluated (Romero & Ventura, 2020). Another example, on how data is data mined
and the specific technique will vary, is public health. de Santos et al. (2019) conducted a study
consisting of 250 articles related to data mining techniques to which it was found that various
sub fields of public health/healthcare systems use different techniques such as but not limited to
data mining and machine learning techniques. Yang et al. (2020) stated that since big data era is
continuously increasing, there are multiple options on different kinds of medical databases and
data mining technologies that can meet the needs of clinical researchers.
References
dos Santos, B. S., Steiner, M. T. A., Fenerich, A. T., & Lima, R. H. P. (2019). Data mining and
machine learning techniques applied to public health problems: A bibliometric analysis from
2009 to 2018.
Computers & Industrial Engineering, 138
, 106120.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated
survey. Wiley interdisciplinary reviews:
Data mining and knowledge discovery, 10
(3), e1355.
Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., ... & Lyu, J. (2020). Brief introduction of
medical database and data mining technology in big data era.
Journal of Evidence‐Based
Medicine, 13
(1), 57-69.
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