<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="min-etal-2022-rred">
    <titleInfo>
        <title>RRED : A Radiology Report Error Detector based on Deep Learning Framework</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dabin</namePart>
        <namePart type="family">Min</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kaeun</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jong</namePart>
        <namePart type="given">Hyuk</namePart>
        <namePart type="family">Lee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yisak</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chang</namePart>
        <namePart type="given">Min</namePart>
        <namePart type="family">Park</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 4th Clinical Natural Language Processing Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tristan</namePart>
            <namePart type="family">Naumann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kirk</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Seattle, WA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Radiology report is an official record of radiologists’ interpretation of patients’ radiographs and it’s a crucial component in the overall medical diagnostic process. However, it can contain various types of errors that can lead to inadequate treatment or delay in diagnosis. To address this problem, we propose a deep learning framework to detect errors in radiology reports. Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework. To compensate for the lack of data availability of radiology reports with errors, we develop an error generator to systematically create artificial errors in existing reports. In addition, we introduce a Medical Knowledge-enhancing Pre-training to further utilize the knowledge of abbreviations and key phrases frequently used in the medical domain. We believe that this is the first work to propose a deep learning framework for detecting errors in radiology reports based on a rich contextual and medical understanding. Validation on our radiologist-synthesized dataset, based on MIMIC-CXR, shows 0.80 and 0.95 of the area under precision-recall curve (AUPRC) and the area under the ROC curve (AUROC) respectively, indicating that our framework can effectively detect errors in the real-world radiology reports.</abstract>
    <identifier type="citekey">min-etal-2022-rred</identifier>
    <identifier type="doi">10.18653/v1/2022.clinicalnlp-1.5</identifier>
    <location>
        <url>https://aclanthology.org/2022.clinicalnlp-1.5</url>
    </location>
    <part>
        <date>2022-07</date>
        <extent unit="page">
            <start>41</start>
            <end>52</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T RRED : A Radiology Report Error Detector based on Deep Learning Framework
%A Min, Dabin
%A Kim, Kaeun
%A Lee, Jong Hyuk
%A Kim, Yisak
%A Park, Chang Min
%Y Naumann, Tristan
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 4th Clinical Natural Language Processing Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, WA
%F min-etal-2022-rred
%X Radiology report is an official record of radiologists’ interpretation of patients’ radiographs and it’s a crucial component in the overall medical diagnostic process. However, it can contain various types of errors that can lead to inadequate treatment or delay in diagnosis. To address this problem, we propose a deep learning framework to detect errors in radiology reports. Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework. To compensate for the lack of data availability of radiology reports with errors, we develop an error generator to systematically create artificial errors in existing reports. In addition, we introduce a Medical Knowledge-enhancing Pre-training to further utilize the knowledge of abbreviations and key phrases frequently used in the medical domain. We believe that this is the first work to propose a deep learning framework for detecting errors in radiology reports based on a rich contextual and medical understanding. Validation on our radiologist-synthesized dataset, based on MIMIC-CXR, shows 0.80 and 0.95 of the area under precision-recall curve (AUPRC) and the area under the ROC curve (AUROC) respectively, indicating that our framework can effectively detect errors in the real-world radiology reports.
%R 10.18653/v1/2022.clinicalnlp-1.5
%U https://aclanthology.org/2022.clinicalnlp-1.5
%U https://doi.org/10.18653/v1/2022.clinicalnlp-1.5
%P 41-52

Markdown (Informal)

[RRED : A Radiology Report Error Detector based on Deep Learning Framework](https://aclanthology.org/2022.clinicalnlp-1.5) (Min et al., ClinicalNLP 2022)

• RRED : A Radiology Report Error Detector based on Deep Learning Framework (Min et al., ClinicalNLP 2022)

ACL

• Dabin Min, Kaeun Kim, Jong Hyuk Lee, Yisak Kim, and Chang Min Park. 2022. RRED : A Radiology Report Error Detector based on Deep Learning Framework. In Proceedings of the 4th Clinical Natural Language Processing Workshop, pages 41–52, Seattle, WA. Association for Computational Linguistics.