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.

翻译：目的：我们的目标是开发并验证TrajVis，这是一种交互式工具，可帮助临床医生利用人工智能（AI）模型，借助患者纵向电子病历（EMR）实现慢性疾病进展的个性化精准管理。方法：我们首先与临床医生和数据科学家进行需求分析，确定TrajVis系统的可视化分析任务及其设计与功能。使用名为DEPOT的慢性肾病（CKD）轨迹推断图AI模型进行系统开发与演示。TrajVis以全栈Web应用程序形式实现，采用源自Atrium Health Wake Forest Baptist转化数据仓库和印第安纳患者护理研究数据库的合成EMR数据。通过与肾病专家的案例研究以及针对临床医生和数据科学家的用户体验调查，对TrajVis系统进行评估。结果：TrajVis临床信息系统由四个面板组成：患者视图（展示人口统计学和临床信息）、轨迹视图（在潜在空间中可视化DEPOT推导的CKD轨迹）、临床指标视图（阐释临床特征的纵向模式并解释DEPOT预测）以及分析视图（展示个体化CKD进展轨迹）。系统评估表明，TrajVis支持临床医生总结临床数据、识别个体化风险预测因子以及可视化患者疾病进展轨迹，克服了AI在医疗保健中应用的障碍。结论：TrajVis弥合了快速发展的AI/ML建模与这些模型在慢性疾病个性化精准管理中的临床使用之间的差距。