Artificial Neural Network Approach in Laboratory Test Reporting Learning Algorithms

Creative Commons License

Demirci F., AKAN P., KÜME T., Sisman A., Erbayraktar Z., Sevinc S.

AMERICAN JOURNAL OF CLINICAL PATHOLOGY, vol.146, no.2, pp.227-237, 2016 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 146 Issue: 2
  • Publication Date: 2016
  • Doi Number: 10.1093/ajcp/aqw104
  • Journal Name: AMERICAN JOURNAL OF CLINICAL PATHOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.227-237
  • Keywords: Neural networks (computer), Machine learning, Biochemistry, Clinical laboratory, Information systems, Autoverification, SYSTEM, AUTOVERIFICATION, CLASSIFICATION
  • Dokuz Eylül University Affiliated: Yes

Abstract

Objectives: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently.