title: Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error.
abstract: 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.
  
  
  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.
authors.affiliation: Department of Pathology, Oregon Health & Science University, Portland, OR 97239, USA. kazmierc@ohsu.edu
authors.forename: Steven C
authors.initials: SC
authors.lastname: Kazmierczak
authors.forename: Todd K
authors.initials: TK
authors.lastname: Leen
authors.forename: Deniz
authors.initials: D
authors.lastname: Erdogmus
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Carreira-Perpinan
mesh_terms: D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D006801:Humans; D007753:Laboratories / Q000458:organization & administration*; D008485:Medical Audit; D010865:Pilot Projects