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Communication Dans Un Congrès Année : 2023

Clustering Corticosteroids Responsiveness in Sepsis Patients using Game-Theoretic Rough Sets

Résumé

Performing data mining tasks in the medical domain poses a significant challenge, mainly due to the uncertainty present in patients' data, such as incompleteness or missingness. In this paper, we focus on the data mining task of clustering corticosteroid (CS) responsiveness in sepsis patients. We address the issue and challenge of missing data by applying Game-Theoretic Rough Sets (GTRS) as a three-way decision approach. Our study considers the APROCCHS cohort, comprising 1240 sepsis patients, provided by the Assistance Publique-Hôpitaux de Paris (AP-HP), France. Our experimental results on the APROCCHS cohort indicate that GTRS maintains the trade-off between accuracy and generality, demonstrating its effectiveness even when increasing the number of missing values.
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Dates et versions

hal-04385087 , version 1 (15-01-2024)

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Citer

Rahma Hellali, Zaineb Chelly Dagdia, Karine Zeitouni. Clustering Corticosteroids Responsiveness in Sepsis Patients using Game-Theoretic Rough Sets. 18th Conference on Computer Science and Intelligence Systems, Sep 2023, Warsaw, Poland. pp.545-556, ⟨10.15439/2023F9521⟩. ⟨hal-04385087⟩
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