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This project is focused on creating an interactive web application with tools specific for transplant graft survival prediction. The project started in 2021 with the motivation of analyzing a database of renal transplants within Guy's and St.Thomas' NHS Trust between 2009-2019. The current version is hence developed with certain assumptions that may only be specific to our database. We hope to create an application that covers most basic use cases for clinician researchers with limited software engineering experience and has the flexibility to allow more advanced users to extend custom models and functions with ease. Patient data is confidential and will remain excluded from this repository.

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Use Cases Limitations Evidence Owner's Insight

The transplant graft survival app offers a suite of tools tailored to clinicians and researchers focusing on transplant outcomes. Through its intuitive interface, users can delve into trends and insights regarding graft survival, leveraging the "Dashboard" feature for visualizing data trends. Moreover, the app harnesses the power of machine learning in the "Predictive Modeling" section, enabling users to train models for accurate graft survival predictions, thereby aiding in treatment planning and patient management. For deeper analysis, the "Experiments" section allows users to conduct customized analyses, fostering a better understanding of transplant outcomes and potential avenues for research. Additionally, advanced users can access beta features and extend the application's functionality through "Developer Mode," facilitating tailored solutions to specific research needs and requirements. Overall, the transplant graft survival app serves as a comprehensive toolset for analyzing transplant data, empowering users with insights to improve patient outcomes and advance research in the field.

See owner's GitHub repository for more information:

This work is currently submitted for peer review. The work was started by a group of transplant surgeons at Guy's and St.Thomas' NHS Foundation Trust who were interested in learning about machine learning in clinical data. Dr Woochan Hwang joined as a junior doctor in the renal department.

Authors: Dr Woochan Hwang, Mr Usman Harron, Mr Ravindhran Bharadhwaj, Mr Torath Ameen, Mr Pankaj Chandak, Miss Zakri Rhana

This application was not uploaded by the author, but through their publicly available Github repository,

License Apache 2.0

Copyright holders Hwang 2022.


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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