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

Autre communication scientifique (congrès sans actes - poster - séminaire...)

Ce document a été publié dans

11th International Conference on Innovations in Bio-Inspired Computing and Applications, 2020-12-16, Virtual. vol. 1372

Springer

Résumé en anglais

The complete blood count (CBC) performed by automated haematology analysers is the most common clinical procedure in the world. Used for health checkup, diagnosis and patient follow-up, the CBC impacts the majority of medical decisions. If the analysis does not fit an expected setting, the laboratory staff manually reviews a blood smear, which is highly time-consuming. Criteria for reviewing CBCs are based on international consensus guidelines and locally adjusted to account for laboratory resources and populations characteristics. Our objective is to provide a clinical laboratory decision support tool to identify which CBC variables are linked to an increased risk of abnormal manual smear and at which threshold values. Thus, we treat criteria adjustment as a feature selection problem. We propose a cost-sensitive Lasso-penalised additive logistic regression combined with stability selection, adapted to the peculiarities of data and context: class-imbalance, categorisation of continuous predictors, required stability and enhanced interpretability. Using simulated and real CBC data, we show that our proposal is competitive in terms of predictive performance (compared to deep neural networks) and model selection performance (provided that there is sufficient data in the neighbourhood of the true thresholds). The R code is publicly available as an open source project.< Réduire

Mots clés en anglais

Categorisation of continuous variables

Data mining

Feature selection

GAM

Imbalance

Interpretability

Lasso

Machine Learning for Healthcare Applications

Population Health

URI

https://oskar-bordeaux.fr/handle/20.500.12278/140238

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219