FHIR Cross-Version Extensions package for FHIR R4 from FHIR R3
0.0.1-snapshot-2 - informative International flag

FHIR Cross-Version Extensions package for FHIR R4 from FHIR R3 - Version 0.0.1-snapshot-2. See the Directory of published versions

ValueSet: Cross-version VS for R3.StatisticsCode for use in FHIR R4

Official URL: http://hl7.org/fhir/3.0/ValueSet/R3-observation-statistics-for-R4 Version: 0.0.1-snapshot-2
Standards status: Informative Maturity Level: 0 Computable Name: R3_observation_statistics_for_R4

This cross-version ValueSet represents concepts from http://hl7.org/fhir/ValueSet/observation-statistics 3.0.2 for use in FHIR R4. Concepts not present here have direct equivalent mappings crossing all versions from R3 to R4.

References

This value set is not used here; it may be used elsewhere (e.g. specifications and/or implementations that use this content)

Logical Definition (CLD)

  • Include these codes as defined in http://hl7.org/fhir/observation-statistics version 3.0.2
    CodeDisplayDefinition
    averageAverageThe [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) of N measurements over the stated period
    maximumMaximumThe [maximum](https://en.wikipedia.org/wiki/Maximal_element) value of N measurements over the stated period
    minimumMinimumThe [minimum](https://en.wikipedia.org/wiki/Minimal_element) value of N measurements over the stated period
    countCountThe [number] of valid measurements over the stated period that contributed to the other statistical outputs
    totalcountTotal CountThe total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values
    medianMedianThe [median](https://en.wikipedia.org/wiki/Median) of N measurements over the stated period
    std-devStandard DeviationThe [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of N measurements over the stated period
    sumSumThe [sum](https://en.wikipedia.org/wiki/Summation) of N measurements over the stated period
    varianceVarianceThe [variance](https://en.wikipedia.org/wiki/Variance) of N measurements over the stated period
    20-percent20th PercentileThe 20th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period
    80-percent80th PercentileThe 80th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period
    4-lowerLower QuartileThe lower [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period
    4-upperUpper QuartileThe upper [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period
    4-devQuartile DeviationThe difference between the upper and lower [Quartiles](https://en.wikipedia.org/wiki/Quartile) is called the Interquartile range. (IQR = Q3-Q1) Quartile deviation or Semi-interquartile range is one-half the difference between the first and the third quartiles.
    5-11st QuintileThe lowest of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population
    5-22nd QuintileThe second of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population
    5-33rd QuintileThe third of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population
    5-44th QuintileThe fourth of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population
    skewSkewSkewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined. Source: [Wikipedia](https://en.wikipedia.org/wiki/Skewness)
    kurtosisKurtosisKurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Source: [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis)
    regressionRegressionLinear regression is an approach for modeling two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variables. Source: [Wikipedia](https://en.wikipedia.org/wiki/Simple_linear_regression) This Statistic code will return both a gradient and an intercept value.

 

Expansion

This value set expansion contains 21 concepts.

CodeSystemDisplayDefinition
  averagehttp://hl7.org/fhir/observation-statisticsAverage

The mean of N measurements over the stated period

  maximumhttp://hl7.org/fhir/observation-statisticsMaximum

The maximum value of N measurements over the stated period

  minimumhttp://hl7.org/fhir/observation-statisticsMinimum

The minimum value of N measurements over the stated period

  counthttp://hl7.org/fhir/observation-statisticsCount

The [number] of valid measurements over the stated period that contributed to the other statistical outputs

  totalcounthttp://hl7.org/fhir/observation-statisticsTotal Count

The total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values

  medianhttp://hl7.org/fhir/observation-statisticsMedian

The median of N measurements over the stated period

  std-devhttp://hl7.org/fhir/observation-statisticsStandard Deviation

The standard deviation of N measurements over the stated period

  sumhttp://hl7.org/fhir/observation-statisticsSum

The sum of N measurements over the stated period

  variancehttp://hl7.org/fhir/observation-statisticsVariance

The variance of N measurements over the stated period

  20-percenthttp://hl7.org/fhir/observation-statistics20th Percentile

The 20th Percentile of N measurements over the stated period

  80-percenthttp://hl7.org/fhir/observation-statistics80th Percentile

The 80th Percentile of N measurements over the stated period

  4-lowerhttp://hl7.org/fhir/observation-statisticsLower Quartile

The lower Quartile Boundary of N measurements over the stated period

  4-upperhttp://hl7.org/fhir/observation-statisticsUpper Quartile

The upper Quartile Boundary of N measurements over the stated period

  4-devhttp://hl7.org/fhir/observation-statisticsQuartile Deviation

The difference between the upper and lower Quartiles is called the Interquartile range. (IQR = Q3-Q1) Quartile deviation or Semi-interquartile range is one-half the difference between the first and the third quartiles.

  5-1http://hl7.org/fhir/observation-statistics1st Quintile

The lowest of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population

  5-2http://hl7.org/fhir/observation-statistics2nd Quintile

The second of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population

  5-3http://hl7.org/fhir/observation-statistics3rd Quintile

The third of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population

  5-4http://hl7.org/fhir/observation-statistics4th Quintile

The fourth of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population

  skewhttp://hl7.org/fhir/observation-statisticsSkew

Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined. Source: Wikipedia

  kurtosishttp://hl7.org/fhir/observation-statisticsKurtosis

Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Source: Wikipedia

  regressionhttp://hl7.org/fhir/observation-statisticsRegression

Linear regression is an approach for modeling two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variables. Source: Wikipedia This Statistic code will return both a gradient and an intercept value.


Explanation of the columns that may appear on this page:

Level A few code lists that FHIR defines are hierarchical - each code is assigned a level. In this scheme, some codes are under other codes, and imply that the code they are under also applies
System The source of the definition of the code (when the value set draws in codes defined elsewhere)
Code The code (used as the code in the resource instance)
Display The display (used in the display element of a Coding). If there is no display, implementers should not simply display the code, but map the concept into their application
Definition An explanation of the meaning of the concept
Comments Additional notes about how to use the code