AVALOS, Marta; TOUCHAIS, Helene; HENRIQUEZ-HENRIQUEZ, Marcela

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AVALOS, Marta
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

TOUCHAIS, Helene
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

HENRIQUEZ-HENRIQUEZ, Marcela

Langue

Communication dans un congrès avec actes

Ce document a été publié dans

ML4H - Machine Learning for Health workshop at NeurIPS 2020, ML4H - Machine Learning for Health workshop at NeurIPS 2020, 2020-12-07, Vancouver / Virtual. 2020

Résumé en anglais

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.< Réduire

Mots clés en anglais

Interpretability

Lasso

GAM

Imbalance

Population Health

URI

https://oskar-bordeaux.fr/handle/20.500.12278/27188

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219