Dissertation
Mining Electronic Health Records: Spatio-Temporal Analysis of Hospital Patient Trajectories
Washington State University
Doctor of Philosophy (PhD), Washington State University
2023
DOI:
https://doi.org/10.7273/000005363
Abstract
The widespread use of Electronic Health Records (EHRs) in the healthcare systems has resulted in a massive amount of patient specific information in digitally archived form. It has opened a door of opportunities for research communities to facilitate clinical studies and improve healthcare quality. Mining meaningful information from this data is challenging due to its inherent complex, noisy nature. In this dissertation, we present our research efforts to overcome some of the challenges associated with mining insight from health records.
In the first part of the dissertation, we present a visual analytics framework to track patient trajectories. We extract the trajectories from medical records and visualize how a variable for example, antibiotic usage, evolves over time along the patients stay in the hospital. In the second part, we analyze patients contact pattern i.e. which patients came in close proximity during their hospital visit. This is particularly important in infectious disease modeling. Finally, in the third part we develop a predictive model to learn from patients trajectories and predict two outcomes simultaneously namely predict patients next unit label and predict how many days the patient will stay in the hospital. Utilizing this approach can aid in clinical decision support and lead to overall improved healthcare planning.
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Details
- Title
- Mining Electronic Health Records: Spatio-Temporal Analysis of Hospital Patient Trajectories
- Creators
- Kaniz Fatema Madhobi
- Contributors
- Ananth AK Kalyanaraman (Advisor)Eric EL Lofgren (Committee Member)Venera VA Arnaoudova (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 102
- Identifiers
- 99901031138401842
- Language
- English
- Resource Type
- Dissertation