Abstract
The increasing quantity of data in biomedical informatics is leading towards better patient profiling and personalized medicine. Lab tests, medical images, and clinical data represent extraordinary sources for patient characterization. While retrospective studies focus on finding correlations in this sheer volume of data, potential new biomarkers are difficult to identify. A common approach is to observe patient mortality with respect to different clinical variables in what is called survival analysis. Kaplan-Meier plots, also known as survival curves, are generally used to examine patient survival in retrospective and prognostic studies. The plot is very intuitive and hence very popular in the medical domain to disclose evidence of poor or good prognosis. However, the Kaplan-Meier plots are mostly static and the data exploration of the plotted cohorts can be performed only with additional analysis. There is a need to make survival plots interactive and to integrate potential prognostic data that may reveal correlations with disease progression. We introduce SurviVIS, a visual analytics approach for interactive survival analysis and data integration on Kaplan-Meier plots. We demonstrate our work on a melanoma dataset and in the perspective of a potential use case in precision imaging.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
