File:Fig2 Beaulieu-JonesJMIRMedInfo2018 6-1.png

Figure 2. Summary of missing data across 143 clinical laboratory measures. (A) After ranking the clinical laboratory measures by the number of total results, the percentage of patients missing a result for each test was plotted (red points). At each rank, the percentage of complete cases for all tests of equal or lower rank were also plotted (blue points). Only variables with a rank ≤75 are shown. The vertical bar indicates the 28 tests that were selected for further analysis. (B) The full distribution of patient median ages is shown in blue, and the fraction of individuals in each age group that had a complete set of observations for tests 1-28 are shown in red. (C) Within the 28 laboratory tests that were selected for imputation analyses, the mean number of missing tests is depicted as a function of age. (D) Within the 28 laboratory tests that were selected for imputation, the mean number of missing tests is depicted as a function of body mass index (BMI). (E) Accuracy of a random forest predicting the presence or absence of all 143 laboratory tests. AUROC: area under the receiver operating characteristic curve. (F) Accuracy of a random forest predicting the presence or absence of the top 28 laboratory tests, by Logical Observation Identifiers Names and Codes (LOINC).

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R. (2018). "Characterizing and managing missing structured data in electronic health records: Data analysis". JMIR Medical Informatics 6 (1): e11. doi:10.2196/medinform.8960. 

2018

Beaulieu-Jones, B.K.; Lavage, D.R.; Snyder, J.W.; Moore, J.H.; Pendergrass, S.A.; Bauer, C.R.

Creative Commons Attribution 4.0 International