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J Biomed Inform. 2016 Feb;59:276-84. doi: 10.1016/j.jbi.2015.12.010. Epub 2015 Dec 19.

An unsupervised learning method to identify reference intervals from a clinical database.

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Center for Biomedical Informatics Research, Stanford University, Stanford, CA, United States. Electronic address:
Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, United States.
Center for Biomedical Informatics Research, Stanford University, Stanford, CA, United States.


Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.


Electronic health record; Laboratory tests; Reference intervals; Unsupervised learning

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