Modeling and computational strategies for medical decision making

Yuan, Fan

Title:

Modeling and computational strategies for medical decision making

dc.contributor.advisor	Lee, Eva K.
dc.contributor.author	Yuan, Fan
dc.contributor.committeeMember	Monteiro, Renato D. C.
dc.contributor.committeeMember	Mei, Yajun
dc.contributor.committeeMember	Sun, Andy
dc.contributor.committeeMember	Chu, James C. H.
dc.contributor.department	Industrial and Systems Engineering
dc.date.accessioned	2016-05-27T13:09:38Z
dc.date.available	2016-05-27T13:09:38Z
dc.date.created	2015-05
dc.date.issued	2015-01-13
dc.date.submitted	May 2015
dc.date.updated	2016-05-27T13:09:38Z
dc.description.abstract	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.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/54857
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Healthcare
dc.subject	Operations research
dc.subject	Data mining
dc.title	Modeling and computational strategies for medical decision making
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.corporatename	H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename	College of Engineering
relation.isOrgUnitOfPublication	29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral