Publication Date
2023
Document Type
Dissertation
Committee Members
Tanvi Banerjee, Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Michael Raymer, Ph.D. (Committee Member); Nirmish Shah, M.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches have been evaluated on physiological, medicinal and pain measurements collected from Electronic Health Records (EHRs), demonstrating their ability to digitize the medical essence of patients, thereby assisting in multiple aspects of clinical decision making in pain management. First, we propose to explore the feasibility of estimating subjective pain from objective physiological signals collected from EHRs irrespective of the nature of hospital visits in large patient cohorts. Second, we propose to learn deep feature representations of the subjective pain trajectories from objective physiological signals collected from EHRs. Third, we propose to learn future pain from historical patient EHR data using time-series forecasting methods. Our initial results indicate promise in pursuing each of these three efforts, and our study can be a valuable addition to ongoing studies that utilize EHR data to help providers better understand and design real-time pain management strategies.
Page Count
116
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2023
Copyright
Copyright 2023, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
