Nenova, Zlatana
(2017)
Essays on the management of appointments for chronic conditions.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Treating chronic conditions is a fairly complex task, which requires well-timed appointments to control one's disease progression. In my dissertation I would like to optimize the monitoring strategies and better predict the demand-for-care of patients with chronic kidney disease (CKD). To do that I design a chronic disease monitoring framework which consists of forecasting, survival analysis and Markov Decision Process (MDP) models. First, I propose a forecasting model which quantifies the impact of CKD-related doctor's appointments on patient's disease progression. The model accounts for patient's comorbidities, vital signs, and important laboratory values. Second, I propose a survival analysis model, which estimates the expected life days of a patient given his or her current health status. Finally, I use the information gained from the first two models to parametrize and solve the MDP, which can suggest monitoring strategies and predict medium-term demand for CKD-patient-care in a clinic. In addition to the chronic disease monitoring framework, I examine CKD patient characteristics associated with a higher resource utilization.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
28 September 2017 |
| Date Type: |
Publication |
| Defense Date: |
5 June 2017 |
| Approval Date: |
28 September 2017 |
| Submission Date: |
31 July 2017 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
144 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Joseph M. Katz Graduate School of Business > Business Administration |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
optimal appointment allocation, chronic disease, Markov Decision Process |
| Date Deposited: |
28 Sep 2017 15:08 |
| Last Modified: |
28 Sep 2017 15:08 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/32949 |
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