TrajVis: a Visual Clinical Decision Support System to Translate Artificial Intelligence Trajectory Models in the Precision Management of Chronic Kidney Disease

Overview

Journal J Am Med Inform Assoc

Publisher Oxford University Press

Specialty Medical Informatics

Date 2024 Jun 25

PMID 38916922

Authors

Zuotian Li

Xiang Liu

Ziyang Tang

Nanxin Jin

Pengyue Zhang

Michael T Eadon

Qianqian Song

Yingjie V Chen

Jing Su

Affiliations

Soon will be listed here.

Abstract

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 (EMRs) for personalized precision management of chronic disease progression.

Materials And 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 DisEase PrOgression Trajectory (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 4 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.

Discussion: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations.

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.

Citing Articles

Reflections on interactive visualization of electronic health records: past, present, future.

Arleo A, Chen A, Gotz D, Kandaswamy S, Bernard J J Am Med Inform Assoc. 2024; 31(11):2423-2428.

PMID: 39429003 PMC: 11491741. DOI: 10.1093/jamia/ocae249.

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