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, cilt.146, sa.2, ss.227-237, 2016 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 146 Sayı: 2
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1093/ajcp/aqw104
  • Dergi Adı: AMERICAN JOURNAL OF CLINICAL PATHOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.227-237
  • Anahtar Kelimeler: Neural networks (computer), Machine learning, Biochemistry, Clinical laboratory, Information systems, Autoverification, SYSTEM, AUTOVERIFICATION, CLASSIFICATION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

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.