Analytics Approaches to the Development of Diabetic Retinopathy Screening Policies

dc.contributor.advisorLim, Gino J.
dc.contributor.advisorLee, Taewoo
dc.contributor.committeeMemberLin, Ying
dc.contributor.committeeMemberWeng, Christina Y.
dc.contributor.committeeMemberDeshmukh, Ashish A.
dc.contributor.committeeMemberPeng, Jiming
dc.creatorDorali, Poria
dc.creator.orcid0000-0003-1736-1008 2023
dc.description.abstractDiabetic retinopathy (DR) is the leading cause of blindness for working-age adults in the US. Over 60% of patients with type II diabetes and 90% of patients with type I diabetes develop DR within 20 years of diagnosis. Routine comprehensive screening examinations have proved effective in detecting early stages of DR and timely treatment can prevent up to 98% of DR-related vision loss. However, only 50-60% of diabetic patients adhere to the current annual screening guidelines. Recently, teleretinal imaging (TRI) has emerged as an accessible screening tool for patients with limited access. However, there exists no well-established guideline that incorporates TRI-based screening for such patients. In this thesis, we study a multi-pronged analytics approach to quantify and evaluate the advantages and limitations of TRI compared with traditional clinic-based screening (CS) and propose new screening policies for patients with limited access to eye care. First, we develop a simulation model that examines the health and cost benefits of various routine CS and TRI-based DR screening policies at different time intervals for various types of diabetic patients. Additionally, we identify patient subgroups who would truly benefit from TRI in terms of health benefits and cost savings. Second, we develop a partially observable Markov decision process (POMDP) model to generate personalized DR screening recommendations that exploit the dynamic interaction of TRI and traditional screening based on each patient’s unique health-related and behavioral factors. Lastly, we develop a decision tree model that establishes interpretable DR screening policies by transforming the complex, POMDP-driven personalized screening policies into policies that are more explainable, implementable, and adoptable in clinical practice.
dc.description.departmentIndustrial Engineering, Department of
dc.format.digitalOriginborn digital
dc.identifier.citationPortions of this document appear in: Dorali, Poria, Zahed Shahmoradi, Christina Y. Weng, and Taewoo Lee. "Cost-effectiveness Analysis of a Personalized, Teleretinal-Inclusive Screening Policy for Diabetic Retinopathy via Markov Modeling." Ophthalmology retina 7, no. 6 (2023): 532-542; and in: Dorali, Poria, Rosangel Limongi, Fariha Kabir Torsha, Christina Y. Weng, and Taewoo Lee. "Utilizing Simulation to Update Routine Diabetic Retinopathy Screening Policies." In 2022 Winter Simulation Conference (WSC), pp. 1128-1139. IEEE, 2022.
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dc.subjectMedical Decision Making
dc.subjectPersonalized Screening
dc.subjectDiabetic Retinopathy
dc.subjectMonte Carlo
dc.titleAnalytics Approaches to the Development of Diabetic Retinopathy Screening Policies
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.terms2025-08-01 College of Engineering Engineering, Department of Engineering of Houston of Science in Industrial Engineering


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