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Scalable and Interpretable Predictive Models for Electronic Health Records

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Pierre Genevès
Nabil Layaïda
Jean-Luc Bosson
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Abstract

Early identification of patients at risk of developing complications during their hospital stay is currently one of the most challenging issues in healthcare. Complications include hospital-acquired infections, admissions to intensive care units, and in-hospital mortality. Being able to accurately predict the patients' outcomes is a crucial prerequisite for tailoring the care that certain patients receive, if it is believed that they will do poorly without additional intervention. We consider the problem of complication risk prediction, such as inpatient mortality, from the electronic health records of the patients. We study the question of making predictions on the first day at the hospital, and of making updated mortality predictions day after day during the patient's stay. We develop distributed models that are scalable and interpretable. Key insights include analysing diagnoses known at admission and drugs served, which evolve during the hospital stay. We leverage a distributed architecture to learn interpretable models from training datasets of gigantic size. We test our analyses with more than one million of patients from hundreds of hospitals, and report on the lessons learned from these experiments.
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Dates and versions

hal-01877742 , version 1 (20-09-2018)

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  • HAL Id : hal-01877742 , version 1

Cite

Amela Fejza, Pierre Genevès, Nabil Layaïda, Jean-Luc Bosson. Scalable and Interpretable Predictive Models for Electronic Health Records. DSAA 2018 - 5th IEEE International Conference on Data Science and Advanced Analytics, Oct 2018, Turin, Italy. pp.1-10. ⟨hal-01877742⟩
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