Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results

• Published in: International journal of laboratory hematology Vol. 44; no. 3; pp. 497 - 503
• Main Authors: Farrell, Christopher‐John L., Giannoutsos, John
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2022
• Subjects: artificial intelligence; artificial neural network; Blood; Blood Cell Count; Blood tests; Computer Simulation; Humans; Laboratories; Learning algorithms; Logistic Models; Machine Learning; mislabelled samples; Neural networks; Neural Networks, Computer; Patients; wrong blood in tube; artificial neural network; mislabelled samples; machine learning; artificial intelligence; wrong blood in tube

Introduction Wrong blood in tube (WBIT) errors are a significant patient‐safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood count (CBC) results against the benchmark of manua...