TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease
CoRR(2024)
摘要
Objective: Our objective is to develop and validate TrajVis, an interactive
tool that assists clinicians in using artificial intelligence (AI) models to
leverage patients' longitudinal electronic medical records (EMR) for
personalized precision management of chronic disease progression. Methods: We
first perform requirement analysis with clinicians and data scientists to
determine the visual analytics tasks of the TrajVis system as well as its
design and functionalities. A graph AI model for chronic kidney disease (CKD)
trajectory inference named DEPOT is used for system development and
demonstration. TrajVis is implemented as a full-stack web application with
synthetic EMR data derived from the Atrium Health Wake Forest Baptist
Translational Data Warehouse and the Indiana Network for Patient Care research
database. A case study with a nephrologist and a user experience survey of
clinicians and data scientists are conducted to evaluate the TrajVis system.
Results: The TrajVis clinical information system is composed of four panels:
the Patient View for demographic and clinical information, the Trajectory View
to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical
Indicator View to elucidate longitudinal patterns of clinical features and
interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD
progression trajectories. System evaluations suggest that TrajVis supports
clinicians in summarizing clinical data, identifying individualized risk
predictors, and visualizing patient disease progression trajectories,
overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis
bridges the gap between the fast-growing AI/ML modeling and the clinical use of
such models for personalized and precision management of chronic diseases.
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