Enhancing Radiological Biomedical Natural Language Processing Tasks with Radiology-Specific Word Encodings: A Comparative Analysis of Word Embeddings Sources
Kamlesh Kumar Yadav 1*, Dhablia Dharmesh Kirit 1
Journal of Angiotherapy 8(9) 1-6 https://doi.org/10.25163/angiotherapy.899873
Submitted: 15 July 2024 Revised: 28 August 2024 Published: 05 September 2024
Abstract
Background: Machine Learning (ML)-based Biomedical Natural Language Processing (BNLP) techniques have garnered attention in radiology. However, these models typically depend on Word Encodings (WE) trained on generic datasets, as radiology-specific word libraries are limited. Objective: This study aimed to investigate the potential of radiography as a comprehensive database for generating Radiology-Specific Word Encodings (RSWE), enhancing the efficiency of BNLP tasks, especially in processing radiological texts. Methods: A systematic evaluation was conducted using WE derived from four databases: medical records, biomedical journals, Wikipedia, and news sources. Unstructured Electronic Medical Record (EMR) data from the Mayo Clinic and PubMed Central publications were used to train WE for medical-specific sources, while GloVe and Google News represented publicly available pre-trained WE for generic sources. Analytical evaluation employed medical keywords in three categories (illness, symptoms, drugs), and a 2-D graphical plot was created for 380 medical words. Numerical evaluation consisted of internal and external assessments. Results: Findings revealed that RSWE derived from EMR and PubMed Central outperformed generic WE, better capturing medical word meanings and identifying medically essential terms, aligning more closely with expert assessments. Conclusion: The study demonstrates the value of radiography as a radiology-specific resource for generating RSWE, with promising implications for improving BNLP in radiology.
Keywords: Radiology-specific word encodings (RSWE), Medical natural language processing (BNLP), Word embeddings (WE), Radiopaedia dataset, Electronic health records (EHR)
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