Date of Award
5-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Bioengineering
Committee Chair/Advisor
Melinda Harman
Committee Member
Delphine Dean
Committee Member
Hai Yao
Committee Member
Casey Hopkins
Committee Member
Paul Heider
Abstract
Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following ostomy surgery. The clinical significance of this work is to better understand practice-level differences and terminology differences in ostomy nursing care for acute-care settings. This dissertation addresses three specific aims. Aim 1 identified machine learning methodologies and their parameters for training natural language processing (NLP) models to extract non-coded data from EHRs. Aim 2 conducted a retrospective analysis on EHRs relevant to ostomy surgery to assess ostomy nursing care and patient outcomes in acute-care systems. AIM 3 developed a taxonomy-based risk model applicable to ostomy nursing care. This dissertation contributes to the growing field of Natural Language Process in healthcare by demonstrating how taxonomy-based classification models can be applied to extract and analyze nurse care data from EHRs. The findings show the critical role of data extraction and bridging the gap between clinical practice and machine learning, ultimately enhancing the usability of nursing-related information within EHR systems.
Recommended Citation
McDonald, LaToya, "Toward the Application of Natural Language Processing in Electronic Health Record Analysis for Taxonomy Development" (2025). All Dissertations. 3905.
https://open.clemson.edu/all_dissertations/3905
Author ORCID Identifier
0009-0001-6860-2521
Included in
Bioinformatics Commons, Biomedical Commons, Biomedical Informatics Commons, Categorical Data Analysis Commons, Comparative and Historical Linguistics Commons, Computational Engineering Commons, Critical Care Nursing Commons, Data Science Commons, Discourse and Text Linguistics Commons, Gastroenterology Commons, Health Communication Commons, Health Services Administration Commons, Health Services Research Commons, Institutional and Historical Commons, Language Description and Documentation Commons, Multivariate Analysis Commons, Nursing Administration Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Other Biomedical Engineering and Bioengineering Commons, Patient Safety Commons, Primary Care Commons, Quality Improvement Commons, Risk Analysis Commons, Surgery Commons, Systems and Communications Commons, Translational Medical Research Commons