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

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Abstract

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.
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Dates and versions

hal-03085426 , version 1 (21-12-2020)

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  • HAL Id : hal-03085426 , version 1

Cite

Marta Avalos, Hélène Touchais, Marcela Henríquez-Henríquez. A decision-making tool to fine-tune abnormal levels in the complete blood count tests. ML4H - Machine Learning for Health workshop at NeurIPS 2020, Dec 2020, Vancouver / Virtual, Canada. ⟨hal-03085426⟩
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