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Automated Clinical Note Section Identification Using Transfer Learning and Contextual Embeddings

Publication Type : Conference Paper

Publisher : IEEE

Source : 2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023; Karnataka; India, DOI: 10.1109/GCITC60406.2023.10425802

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191735542&doi=10.1109%2fGCITC60406.2023.10425802&partnerID=40&md5=ec08a3805c0f4196885081af1caea46f

Campus : Amritapuri

School : School of Computing

Center : AmritaCREATE

Year : 2023

Abstract : The lack of structured formatting in clinical notes, often generated during patient consultations, contradicts established clinical practice guidelines advocating for structured formats such as SOAP. Building upon previous clinical note section identification research, this study employs Transfer Learning models integrated with clinical contextual embeddings for the automated classification of clinical notes into distinct major SOAP sections. Validated on a specialized dataset focusing on the cardiology department, the study substantiates the feasibility of developing intelligent note-taking applications using Transfer Learning. The potential for integration into Hospital Information Systems is highlighted, promising to streamline clinical note composition and address the issue of physician burnout. © 2023 IEEE.

Cite this Research Publication : Nair, N., Achan, P., Nedungadi, P., Nair, S., "Automated Clinical Note Section Identification Using Transfer Learning and Contextual Embeddings," 2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023; Karnataka; India, DOI: 10.1109/GCITC60406.2023.10425802

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