Extending Support Vector Regression to Constraint Optimization: Application to the Reduction of Potentially Avoidable Hospitalizations - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2019

Extending Support Vector Regression to Constraint Optimization: Application to the Reduction of Potentially Avoidable Hospitalizations

Abstract

It has been identified that reducing potentially avoidable hospitalizations (PAHs) not only enhances patients' quality of life but also could save substantial costs due to patient treatments. In addition, some recent studies have suggested that increasing the number of nurses in selected geographic areas could lead to the reduction of the rates of potentially avoidable hospitalizations in those areas. In the meantime, health authorities are highly interested in solutions improving health care services to reduce the potentially avoidable hospitalizations. The first approaches could be based on descriptive statistics such as actual rates of potentially avoidable hospitalizations at the geographic area level. These simple approaches have limitations since they do not consider other potential factors associated to the high rates of potentially avoidable hospitalizations. Therefore, in this paper, we propose an approach using support vector machine for regression to select not only the geographic areas but also the number of to-be-added nurses in these areas for the biggest reduction of potentially avoidable hospitalizations. In this approach, besides considering all the potential factors, we also take into account the constraints related to the budget and the equality of health care access. In our work, we specifically apply the approach on the Occitanie, France region and geographic areas mentioned above are the cross-border living areas (fr. Bassins de vie-BVs). As we aim at building a user-friendly decision support system, the results of our work are visualized on spatial maps. Although our work is on a specific region and geographic areas, our approach can be extended at the national level or to other regions or countries. Moreover, in this paper, the other methods for regression are also introduced and evaluated as parts of our work.
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Dates and versions

lirmm-02054874 , version 1 (07-10-2019)

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Huu Tu Ngo, Vera Georgescu, Carmen Gervet, Anne Laurent, Thérèse Libourel Rouge, et al.. Extending Support Vector Regression to Constraint Optimization: Application to the Reduction of Potentially Avoidable Hospitalizations. 3rd Workshop on Data Science for Social Good (SoGood 2018) - ECML/PKDD Workshop, Sep 2018, Dublin, Ireland. pp.89-102, ⟨10.1007/978-3-030-13453-2_8⟩. ⟨lirmm-02054874⟩
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