To accomplish this, quantitation of the correlation of a data element to an outcome and the underlying quality of the data element itself is performed.
Each step yields a data element, for instance the date of surgery, or a secondary diagnosis (comorbidity).
For example, a data element called "grade of tumor" can be collected using various formats (e.g. some collect "grade-1, grade-2, and grade-3" vs. others who collect "low grade, intermediate grade, and high grade").
The analysis of privacy implications of data types revealed that privacy mainly depends on the interplay of a data element's content, the extent and granularity of user control, and its concrete implementation.
The discussion of data types revealed that privacy mainly depends on the interplay of a data element's content, the extent and granularity of user control, and its concrete implementation.
As a general rule, the user creating a data element becomes the owner of this element and has exclusive rights for editing and deleting.
These observations indicate that application of the E-score to a given data element in many cases can give the investigator a measurement of the utility of a data element beyond p value alone.
By the mapping from data element to illness, we mean the identification of a single value of a data element with a particular illness, injury or condition.
Data architecture taxonomies classify, identify, and describe the content of a data element.
The price paid to store the matrix in each node is compensated by not computing these distances, especially if a data element occupies a large amount of bytes and if the distance function is expensive.
