BioBridge: Unified Bio-Embedding With Bridging Modality in Code-Switched EMR

• Published in: IEEE access Vol. 12; pp. 141866 - 141877
• Main Authors: Jeon, Jangyeong, Cho, Sangyeon, Lee, Dongjoon, Lee, Changhee, Kim, Junyeong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; code-switching; Coders; Context; Electronic health records; electronic medical record; Electronic medical records; Embedding; emergency department; Emergency medical services; Emergency services; Encoding; Feature extraction; Machine learning; Modules; Natural language processing; pediatric emergency department; Pediatrics; Source code; Training; Unified modeling language
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3467251

Pediatric Emergency Department (PED) overcrowding presents a significant global challenge, prompting the need for efficient solutions. This paper introduces the BioBridge framework, a novel approach that applies Natural Language Processing (NLP) to Electronic Medical Records (EMRs) in written free-text form to enhance decision-making in PED. In non-English speaking countries, such as South Korea, EMR data is often written in a Code-Switching(CS) format that mixes the native language with English, with most code-switched English words having clinical significance. The BioBridge framework consists of two core modules: "bridging modality in context" and "unified bio-embedding." The "bridging modality in context" module improves the contextual understanding of bilingual and code-switched EMRs. In the "unified bio-embedding" module, the knowledge of the model trained in the medical domain is injected into the encoder-based model to bridge the gap between the medical and general domains. Experimental results demonstrate that the proposed BioBridge significantly performance traditional machine learning and pre-trained encoder-based models on several metrics, including F1 score, area under the receiver operating characteristic curve (AUROC), area under the precision-recall Curve (AUPRC), and Brier score. Specifically, BioBridge-XLM achieved enhancements of 0.85% in F1 score, 0.75% in AUROC, and 0.76% in AUPRC, along with a notable 3.04% decrease in the Brier score, demonstrating marked improvements in accuracy, reliability, and prediction calibration over the baseline XLM model. The source code will be made publicly available at https://github.com/jjy961228/BioBridge .