Authoring Visualisation of Routinely Collected Data Using LLMs
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Date
2024
Authors
Journal Title
Journal ISSN
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Publisher
The Eurographics Association
Abstract
The integration of routinely collected healthcare data into decision-making processes has the potential to revolutionise patient care and health outcomes. However, the complexity and heterogeneity of these datasets pose significant challenges for effective querying and analysis. Visualisation supports socio-technical processes where data analytics are augmented with human expertise to overcome data complexity. However, the authorship of effective visualisation is a challenging task, especially for users without a technical background, such as commissioners, clinicians and population health experts. This complexity calls for more efforts to develop natural language interfaces (NLIs) to democratise access to and understanding of routine data through visualisation. This short paper presents an innovative approach utilising Large Language Models (LLMs) to facilitate the querying and visualisation of routinely collected healthcare data. We present a preliminary framework for combining natural language queries with visualisation recommendation systems to retrieve and visualise relevant information from electronic health records (EHRs). We propose a human-in-the-loop approach for establishing accurate and efficient LLM-enabled information retrieval. Our preliminary findings suggest that LLMs can significantly streamline the visualisation authoring process, enabling stakeholders and healthcare professionals to access critical information rapidly and accurately. This work underscores the potential of LLM-driven solutions in advancing healthcare data utilisation and paves the way for future research in this promising intersection of artificial intelligence and medical informatics.
Description
CCS Concepts: Human-centered computing → Information visualization; Natural language interfaces
@inproceedings{10.2312:cgvc.20241228,
booktitle = {Computer Graphics and Visual Computing (CGVC)},
editor = {Hunter, David and Slingsby, Aidan},
title = {{Authoring Visualisation of Routinely Collected Data Using LLMs}},
author = {Hosseini, Amir and Wood, Jo and Elshehaly, Mai},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-249-3},
DOI = {10.2312/cgvc.20241228}
}