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.
ABSTRACT
This study investigated how music tempo impacted drivers' fatigue and quality of attention in a long-distance monotonous highway environment. Sixteen drivers were enrolled in four sessions of real-road driving tests under the following four music conditions: no music, slow tempo, medium tempo and fast tempo. Specifically, the drivers' electroencephalogram parameters and eye movement parameters were recorded to measure their extent of fatigue and quality of attention, respectively. Of the three tempos, medium-tempo music is the best choice to reduce fatigue and maintain attention for a long-distance driving. Slow-tempo music can temporarily boost the quality of attention, but after a long period of driving, it significantly deteriorates the driver's levels of fatigue and attention. Fast-tempo music helps relieve driver fatigue but significantly deteriorates drivers' attention after an extended driving time. This study offered practical references for drivers regarding the use of music to avoid fatigue, maintain attention and improve their driving safety. Based on previous theories of music and driving, we have explored the underlying mechanism of how music tempo maintains the alertness of drivers.
