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Book Sections Year : 2022

Tracking Temporal Clusters from Patient Networks

Abstract

Creating homogeneous groups (clusters) of patients from medico-administrative databases provides a better understanding of health determinants. But in these databases, patients have truncated care pathways. We developed an approach based on patient networks to construct care trajectories from such truncated data. We tested this approach on antithrombotic treatments prescribed from 2008 to 2018 contained in the échantillon généraliste des bénéficiaires (EGB). We constructed a patient network for each patients’ age (years from birth). We then applied the Markov clustering algorithm in each network. The care trajectories were finally constructed by matching clusters identified in two consecutive networks. We calculated the silhouette score to assess the performance of this network approach compared to three existing approaches. We identified 12 care trajectories that we were able to associate with pathologies. The best silhouette score was obtained for the network approach. Our approach allowed to highlight care trajectories taking into account the longitudinal, multidimensional and truncated nature of data from medico-administrative databases.

Dates and versions

hal-03911967 , version 1 (23-12-2022)

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Judith Lambert, Anne-Louise Leutenegger, Anne-Sophie Jannot, Anaïs Baudot. Tracking Temporal Clusters from Patient Networks. Challenges of Trustable AI and Added-Value on Health, IOS Press, 2022, Studies in Health Technology and Informatics, ⟨10.3233/SHTI220427⟩. ⟨hal-03911967⟩
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