A decision-making tool to fine-tune abnormal levels in the complete blood count tests

• Main Authors: Avalos-Fernandez, Marta, Touchais, Helene, Henriquez-Henriquez, Marcela
• Format: Journal Article
• Language: English
• Published: 11-11-2020
• Subjects: Computer Science - Learning; Statistics - Applications; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2011.05900

The complete blood count (CBC) performed by automated hematology analyzers is
one of the most ordered laboratory tests. It is a first-line tool for assessing
a patient's general health status, or diagnosing and monitoring disease
progression. When the analysis does not fit an expected setting, technologists
manually review a blood smear using a microscope. The International Consensus
Group for Hematology Review published in 2005 a set of criteria for reviewing
CBCs. Commonly, adjustments are locally needed to account for laboratory
resources and populations characteristics. Our objective is to provide a
decision support tool to identify which CBC variables are associated with
higher risks of abnormal smear and at which cutoff values. We propose a
cost-sensitive Lasso-penalized additive logistic regression combined with
stability selection. Using simulated and real CBC data, we demonstrate that our
tool correctly identify the true cutoff values, provided that there is enough
available data in their neighbourhood.