UC-Shiny
PORTFOLIO OF GIS TOOLS OFFERED BY THE COMPUTATIONAL ONCOLOGY UNIT
Cancer data can come from many different sources and can be difficult to gather and analyze to make conclusions. Our unit developed Some of these applications independently, and others were adopted in collaboration with other cancer centers to tackle these challenges. These tools gather publicly available cancer and sociodemographic data in one place, allowing researchers, clinicians, and private citizens to visualize it using maps, scatterplots, heatmaps, and AI/ML. The Computational Oncology Unit provides these for free to the public so that more time can be dedicated to research and data analysis and not worrying about building custom applications.
UC-Shiny is a data-gathering and visualization platform designed to analyze the burden of cancer in specific geographic areas. It was developed by the Computational Oncology and Bioinformatics team at the University of Chicago Comprehensive Cancer Center.
UC-Shiny includes two main platforms:
- Intelligent Catchment Analysis Tool (iCAT Developed by COBU-UC-CCC):
- Purpose: Identifies and addresses healthcare disparities using AI and Machine Learning. iCAT allows users to upload and analyze their data securely.
- Features:
- Users can select primary and explanatory variables for in-depth analysis.
- Supports machine learning models, including linear, lasso, and elastic net regression.
- Displays chosen models and variables before analysis.
- Calculates performance metrics such as R² Score, Mean Absolute Error (MAE), and Mean Squared Error (MSE).
- Cancer in Focus (CIF developed by UK-MCCC):
- Data Gathering: Inputs data from U.S. counties and retrieves data from NIH, NCI, CDC, and FDA.
- Data Processing: Organizes data by category and geographic level for streamlined analysis.
- Interactive Mapping: Processed data can be integrated into the Cancer InFocus Shiny application for online mapping.
- Efficiency: Facilitates quick updates, improving the characterization of cancer burden and focusing efforts on enhancing outcomes.
These tools empower users to analyze cancer-related data and tackle healthcare disparities with advanced analytics.
Cancer InFocus is a data gathering and visualization platform designed to help people understand the cancer burden in a geographic area. The University of Chicago version was built using the platform developed by the UK Markey Comprehensive Cancer Center. InFocus provides various tools out of the box for analyzing cancer disparities in our catchment area and a platform to build and develop our tools as needed.
Intelligent Catchment Analysis Tool (iCAT)
The Intelligent Catchment Analysis Tool (iCAT)-, developed in-house by the Computational Oncology Unit at UChicago, is designed to identify and address healthcare disparities across specific regions. Powered by artificial intelligence and machine learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities.
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