Year: 2024
Type: conference-paper
Abstract: With the rapid growth of data and the increasing complexity of health information, there is a pressing need for advanced tools that facilitate data interpretation and support evidence-based decision-making. Visualizing scientific evidence is crucial for transforming complex data into actionable insights in public health. Natural Language Interfaces (NLIs) offer a promising approach, enabling users to efficiently navigate vast textual data, uncover hidden insights, and enhance information extraction (IE) processes. This study conceptualizes a prototype taxonomy for systematic review data, streamlining key processes like query interpretation, data transformation, and visual mapping to enhance evidence synthesis and decision-making, empowering decision-makers to make informed, evidence-based choices. We present a conceptual design that enhances user engagement and enables intuitive interactions with the visualization pipeline, simplifying the complexities of information extraction and improving data interpretability for effective decision-making in public health.
Authors: Bahar İlgen, Georges Hattab
Institutions: Robert Koch Institute, Freie Universität Berlin
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Bundesministerium für Gesundheit
OpenAlex ID: W4409403810