arXiv subjects

Ambre Assor

Publications and source records attributed to Ambre Assor.

3 recordsLinked to original sources

arXiv2025

Understanding Expert Exploration in EHR Visualization Tools: The ParcoursVis Use Case

We introduce our ongoing work toward an insight-based evaluation methodology aimed at understanding practitioners' mental models when exploring medical data. It is based on ParcoursVis, a Progressive Visual Analytics system designed to visualize event sequences derived from Electronic Health Records at scale (millions of patients, billions of events), developed in collaboration with the Emergency Departments of 16 Parisian hospitals and with the French Social Security. Building on prior usability validation, our current evaluation focuses on the insights generated by expert users and aims to better understand the exploration strategies they employ when engaging with exploration visualization tools. We describe our system and outline our evaluation protocol, analysis strategy, and preliminary findings. Building on this approach and our pilot results, we contribute a design protocol for conducting insight-based studies under real-world constraints, including the availability of health practitioners whom we were fortunate to interview. Our findings highlight a loop, where the use of the system helps refine data variables identification and the system itself. We aim to shed light on generated insights, to highlight the utility of exploratory tools in health data analysis contexts.

cs.HC↗

arXiv2025

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention

We propose leveraging Large Language Models (LLMs) as an interaction layer for medical visualization systems. In domains like healthcare, where users must navigate high-dimensional, coded, and heterogeneous datasets, LLM-generated queries enable expert medical users to express complex analytical intents in natural language. These intents are then translated into editable and executable queries, replacing the dynamic query interfaces used by traditional visualization systems built around sliders, check boxes, and drop-downs. This interaction model reduces visual clutter and eliminates the need for users to memorize field names or system codes, supporting fluid exploration, with the drawback of not exposing all the filtering criteria. We also reintroduce dynamic queries on demand to better support interactive exploration. We posit that medical users are trained to know the possible filtering options but challenged to remember the details of the attribute names and code values. We demonstrate this paradigm in ParcoursVis, our scalable EventFlow-inspired patient care pathway visualization system powered by the French National Health Data System, one of the largest health data repositories in the world.

cs.HC↗

arXiv2025

ParcoursVis: Visualization of Electronic Health Record Sequences at Scale

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.

cs.HC↗