Date of Award

12-17-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Information Science

First Advisor

Daniel Berleant

Abstract

In clinical settings, technological advancements have facilitated health care improvements in data analytics, artificial intelligence, telemedicine, health information systems, etc. This has furthered our understanding of cancer biology and treatment mechanisms. In this study, we aim to understand whether Moore’s law-like models may derive from historical cancer survival data, and how they can predict survival statistics for newly diagnosed cancer patients. Historically these predictions have previously been done with the diagnosis year as the independent variable and the survival as a dependent variable. In this study we use death year data as an independent variable and from that, we derive 5-, 10- and 20-year survival times. This will help us determine the best fitting curves for determining recent cancer survival times as well as future survival times. To do so, we use publicly available SEER data to obtain average cancer survival time data while avoiding recency bias.

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Biostatistics Commons

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