Quantifying Precancerous Colonic Neoplasia Dynamics and Optimizing Surveillance Policies for Improved Patient Outcomes

Quantifying Precancerous Colonic Neoplasia Dynamics and Optimizing Surveillance Policies for Improved Patient Outcomes

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Description

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths among men and the fourth leading cause among women in the United States. This study aims to 1) investigate the dynamics of precancerous colonic neoplasia and their progression to colorectal cancer, and 2) develop colorectal cancer surveillance strategies to optimize patient health outcomes. To achieve these goals, we analyze colonoscopy reports from over 40,000 patients collected from four major VA hospitals. Statistical models are developed to capture the dynamics of CRC progression. These insights are then integrated into a reinforcement learning framework to design personalized CRC surveillance policies, ultimately enhancing patients quality of life.

About the Author

Mahboubeh (Marjan) Madadi expertise is in the areas of operations research, applied probability and statistics, machine learning (ML), and artificial intelligence (AI). Her research primarily focuses on optimizing stochastic systems and developing data-driven decision support systems using statistical and mathematical models, along with ML and reinforcement learning techniques. Her research spans both healthcare and manufacturing domains. In healthcare, Dr. Madadi specializes in predictive analysis of patient outcomes and the development of models aimed at optimizing patient health through personalized care. In manufacturing, she has designed predictive and prescriptive models to enhance system operations. Dr. Madadi’s work has received funding from leading organizations, including the Department of Energy (DOE) and the National Institutes of Health (NIH).

Date of Event

Spring 4-30-2025

Disciplines

Computer Engineering | Medicine and Health Sciences

Quantifying Precancerous Colonic Neoplasia Dynamics and Optimizing Surveillance Policies for Improved Patient Outcomes

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