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bioRxiv · 10.1101/2023.07.13.548794

ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis

Agraz, M.·Karniadakis, G. E.
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Abstract

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results. Authors summaryMelih Agraz, after graduating from Dokuz Eylul University in Izmir, Turkiye, with a major in Mathematics from the Department of Education, he pursued his Masters degree at the same university in the field of Statistics. He furthered his education by obtaining a Ph.D. in Statistics from the Middle East Technical University, Ankara in Turkiye. As part of his academic journey, he also served as a Fulbright postdoctoral researcher at UC Berkeley in the United States. Following this, he worked as a postdoctoral researcher at Brown University and Beth Israel Hospital of Harvard Medical School. Currently, he holds the position of Assistant Professor in the Department of Statistics at Giresun University in Turkey. George Em Karniadakis is Professor of Applied Mathematics and Engineering at Brown University. He is a member of the National Academy of Engineering of USA. His interests include stochastic multiscale modeling of physical and biological systems, physics-informed machine learning, and deep neural operators. He has co-authored over 500 papers and five books, and he has the highest h-index in Applied Mathematics according to Google Scholar.

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Agraz, M., Karniadakis, G. E.. 2023-07-15. ChatGPT-Enhanced ROC Analysis (CERA): A Shiny Web Tool for Finding Optimal Cutoff in Biomarker Analysis. https://doi.org/10.1101/2023.07.13.548794

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