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Fan Yuan
Title: Modeling and Computational Strategies for Medical Decision Making
Advisor: Dr. Eva K Lee
Committee members: Dr. Yajun Mei, Dr. Renato Monteiro, Dr. Andy Sun, and Dr. James Chu (Rush University)
Date, time, and venue: Wednesday, November 19th, 3:00 PM, Groseclose, 204
Thesis Summary:
Technological advances in prevention, diagnosis, and treatment of diseases help predict disease, prolong life, and promote health. However, with the increase in volume and complexity of data and evidence, medical decision making can be a complex process. Many decisions involve uncertainties and tradeoffs, and can have serious consequences to patients and the clinical practice. For example, to design a radiation therapy treatment plan, physicians must determine the locations of over 50 seeds to deliver precise dosage to the tumor such that the cancer cells are killed while the functionalities of surrounding organs are preserved. To make such complex decisions, providers must balance the potential harm and benefit of medical interventions. Computational methods such as mathematical programming, simulation, and classification have found broad applications in these areas.
In this dissertation, we investigate three topics: predictive models for disease diagnosis and patient behavior, optimization for cancer treatment planning, and public health decision making for infectious disease prevention.
In the first topic, we propose a multi-stage classification framework that incorporates Particle Swarm Optimization (PSO) for feature selection and discriminant analysis via mixed integer programming (DAMIP) for classification. By utilizing the reserved judgment region, it allows the classifier to delay making decisions on ‘difficult-to-classify’ observations and develop new classification rules in later stage. We apply the framework to four real-life medical problems: 1) Patient readmissions: identifies the patients in emergency department who return within 72 hours using patient’s demographic information, complaints, diagnosis, tests, and hospital real-time utility. 2) Flu vaccine responder: predicts high/low responders of flu vaccine on subjects in 5 years using gene signatures. 3) Knee reinjection: predicts whether a patient needs to take a second surgery within 3 years of his/her first knee injection and tackles with missing data. 4) Alzheimer’s disease: distinguishes subjects in normal, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) groups using neuropsychological tests.
In the second topic, we first investigate multi-objective optimization approaches to determine the optimal dose configuration and radiation seed locations in brachytherapy treatment planning. Tumor dose escalation and dose-volume constraints on critical organs are incorporated to kill the tumor while preserving the functionality of organs. Based on the optimization framework, we propose a non-linear optimization model that optimizes the tumor control probability (TCP). The model is solved by a solution strategy that incorporates piecewise linear approximation and local search.
In the third topic, we study optimal strategies for public health emergencies under limited resources. First we investigate the vaccination strategies against a pandemic flu to find the optimal strategy when limited vaccines are available by constructing a mathematical model for the course of the 2009 H1N1 pandemic flu and the process of the vaccination. Second, we analyze the cost-effectiveness of emergency response strategies again a large-scale anthrax attack to protect the entire regional population.
