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Contextual-Aware Information Extractor with Adaptive Objective for Chinese Medical Dialogues

Published:07 June 2022Publication History
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Abstract

Electronic Medical Records (EMRs) are the foundation of modern medical information systems. Despite the benefits of EMRs, the exhausting process of constructing EMRs decreases the efficiency of medical consultation. Therefore, it becomes an emerging research field to automatically extract EMRs from medical dialogues. In Chinese medical dialogues, the phenomena of omission and reference are extremely common, leading to strong contextual relevance among utterances. However, recent studies on converting Chinese medical dialogues to EMRs lack a reliable mechanism to effectively exploit the contextual relevance information among utterances. Moreover, they neglect the frequency imbalance of different items and treat these items indiscriminately, which eventually degrade the overall system performance. In this article, we proposed a Contextual-Aware Information Extractor (CANE), which employs a local-to-global mechanism over utterances to model the contextual relevance among utterances. Furthermore, an adaptive objective is introduced to alleviate the frequency imbalance of items by dynamically assigning weights to each sample. Experimental results indicate that CANE outperforms previous state-of-the-arts with considerable improvements (+6.11% and +3.39% on F1-score).

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 21, Issue 5
        September 2022
        486 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3533669
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 June 2022
        • Online AM: 3 February 2022
        • Accepted: 1 January 2022
        • Revised: 1 October 2021
        • Received: 1 May 2021
        Published in tallip Volume 21, Issue 5

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