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Filtering Synthetic Longitudinal Trajectories with Large Language Models as Synthetic Experts

Year: 2025

Type: conference-paper

Abstract: Introduction: Generating synthetic longitudinal data that accurately captures correlation structures within multivariate measurements and over time remains a challenge. When deep learning models are used for synthetic data generation, they often fail to accurately capture clinical patterns. To distinguish [for full text, please go to the a.m. URL]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25gmds069

Authors: Schächter, Clemens, Pechmann, Astrid, Kirschner, Janbernd, Binder, Harald

Institutions: University of Freiburg, University Medical Center Freiburg

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7103887987

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