Leveraging machine learning and NLP for enhanced cohorting and RxNorm mapping in Electronic Health Records (EHRs)
New Jersey Institute of Technology, Software Engineer, 6060, ViIllage Bend Dr, Dallas, TX, Dallas, Texas.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(02), 141–149.
Article DOI: 10.30574/wjaets.2024.11.2.0083
Publication history:
Received on 29 January 2024; revised on 06 March 2024; accepted on 08 March 2024
Abstract:
This work addresses the combination of machine learning (ML) and natural language processing (NLP) approaches to optimize the process of courting and RxNorm mapping inside Electronic Health Records (EHRs). Cohorting patients based on comparable traits or diseases is vital for clinical research, but it generally depends on time-consuming manual techniques and is prone to mistakes. Similarly, mapping pharmaceutical names to standardized codes such as RxNorm promotes interoperability and data analysis but may be challenging owing to variances in how drugs are reported. Leveraging ML and NLP may automate and optimize these procedures, leading to more efficient cohort identification and precise medication mapping. We offer a thorough technique for integrating ML and NLP algorithms in EHR systems, including data preparation, feature engineering, model training, and assessment. Through testing and analysis, we show the usefulness of our technique in enhancing cohorting accuracy and RxNorm mapping precision. The findings underline the promise of ML and NLP in revolutionizing EHR data management, leading to improved patient care and simplified research procedures.
Keywords:
Machine Learning; Natural Language Processing; Electronic Health Records; Cohorting; RxNorm Mapping; Healthcare Informatics
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0