Steven C. Kazmierczak, 11Department of Pathology, Oregon Health & Science University, Portland, OR, USA
Todd K. Leen, 22Department of Computer Science and Engineering, OGI School of Science and Engineering, Portland, OR, USA
Deniz Erdogmus, 33Department of Computer Science and Engineering, OGI School of Science and Engineering, Portland, OR, USA
Miguel A. Carreira-Perpinan, 44Department of Computer Science and Engineering, OGI School of Science and Engineering, Portland, OR, USA
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.
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.
Clin Chem Lab Med 2007;45:749–52.
