Abstract
Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD- 10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.
Original language | English |
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Title of host publication | The 57th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Student Research Workshop, July 28 - August 2, 2019, Florence, Italy |
Publisher | Association for Computational Linguistics |
Number of pages | 9 |
ISBN (Print) | 9781950737475 |
DOIs | |
Publication status | Published - 2019 |
Event | Association for Computational Linguistics. Meeting - Duration: 28 Jul 2019 → … |
Conference
Conference | Association for Computational Linguistics. Meeting |
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Period | 28/07/19 → … |
Open Access - Access Right Statement
ACL materials are Copyright © 1963–2019 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).Keywords
- classification
- data processing
- distributed databases
- medical informatics
- medical records
- natural language processing (computer science)