Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results
Author:
Affiliation:
1. New South Wales Health Pathology, Nepean Hospital Penrith NSW Australia
Publisher
Wiley
Subject
Biochemistry (medical),Clinical Biochemistry,Hematology,General Medicine
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/ijlh.13820
Reference11 articles.
1. Wrong blood in tube - potential for serious outcomes: can it be prevented?
2. Patient Misidentification in Laboratory Medicine: A Qualitative Analysis of 227 Root Cause Analysis Reports in the Veterans Health Administration
3. Development and characterization of neural network-based multianalyte delta checks
4. Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors
5. Identifying mislabelled samples: Machine learning models exceed human performance
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2. A Machine Learning Model for the Routine Detection of “Wrong Blood in Complete Blood Count Tube” Errors;Clinical Chemistry;2023-07-20
3. Clinlabomics: leveraging clinical laboratory data by data mining strategies;BMC Bioinformatics;2022-09-24
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