Reduction of multi-dimensional laboratory data to a two-dimensional plot: A novel technique for the identification of laboratory error

Steven (Steve) Kazmierczak, Todd K. Leen, Deniz Erdogmus, Miguel A. Carreira-Perpinan

Research output: Contribution to journalArticle

2 Scopus citations


Background: The clinical laboratory generates large amounts of patient-specific data. Detection of errors that arise during pre-analytical, analytical, and post-analytical processes is difficult. We performed a pilot study, utilizing a multidimensional data reduction technique, to assess the utility of this method for identifying errors in laboratory data. Methods: We evaluated 13,670 individual patient records collected over a 2-month period from hospital inpatients and outpatients. We utilized those patient records that contained a complete set of 14 different biochemical analytes. We used two-dimensional generative topographic mapping to project the 14-dimensional record to a two-dimensional space. Results and conclusions: The use of a two-dimensional generative topographic mapping technique to plot multi-analyte patient data as a two-dimensional graph allows for the rapid identification of potentially anomalous data. Although we performed a retrospective analysis, this technique has the benefit of being able to assess laboratory-generated data in real time, allowing for the rapid identification and correction of anomalous data before they are released to the physician. In addition, serial laboratory multi-analyte data for an individual patient can also be plotted as a two-dimensional plot. This tool might also be useful for assessing patient wellbeing and prognosis.

Original languageEnglish (US)
Pages (from-to)749-752
Number of pages4
JournalClinical Chemistry and Laboratory Medicine
Issue number6
Publication statusPublished - Jun 1 2007



  • Data reduction techniques
  • Error detection
  • Laboratory error
  • Serial data analysis

ASJC Scopus subject areas

  • Clinical Biochemistry

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