ParcoursVis: Visualization of Electronic Health Record Sequences at Scale
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914048155582464 |
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| author | Assor, Ambre Sereno, Mickael Fekete, Jean-Daniel |
| author_facet | Assor, Ambre Sereno, Mickael Fekete, Jean-Daniel |
| contents | We present ParcoursVis, an open-source Progressive Visual Analytics tool designed to explore aggregated electronic health record sequences of patients at scale. Existing tools are limited to about 20k patients that they can process fast enough to remain interactive, under human latency limits. They need to process the whole dataset before showing the visualization, taking a time proportional to the data size. Yet, managing large datasets allows for discovering rare medical conditions and unexpected patient pathways, contributing to improving treatments. To overcome this limitation, ParcoursVis relies on a progressive aggregation algorithm that quickly computes an approximate initial result, visualized as an Icicle tree, and improves it iteratively, until the whole computation is done. With its architecture, ParcoursVis remains interactive while visualizing the sequences of millions of patients -- three orders of magnitude more than similar tools. We describe our PVA architecture, which achieves scalability with fast convergence and visual stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ParcoursVis: Visualization of Electronic Health Record Sequences at Scale Assor, Ambre Sereno, Mickael Fekete, Jean-Daniel Human-Computer Interaction We present ParcoursVis, an open-source Progressive Visual Analytics tool designed to explore aggregated electronic health record sequences of patients at scale. Existing tools are limited to about 20k patients that they can process fast enough to remain interactive, under human latency limits. They need to process the whole dataset before showing the visualization, taking a time proportional to the data size. Yet, managing large datasets allows for discovering rare medical conditions and unexpected patient pathways, contributing to improving treatments. To overcome this limitation, ParcoursVis relies on a progressive aggregation algorithm that quickly computes an approximate initial result, visualized as an Icicle tree, and improves it iteratively, until the whole computation is done. With its architecture, ParcoursVis remains interactive while visualizing the sequences of millions of patients -- three orders of magnitude more than similar tools. We describe our PVA architecture, which achieves scalability with fast convergence and visual stability. |
| title | ParcoursVis: Visualization of Electronic Health Record Sequences at Scale |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.10700 |