@inproceedings{ohtsuka-etal-2023-automated,
title = "Automated Orthodontic Diagnosis from a Summary of Medical Findings",
author = "Ohtsuka, Takumi and
Kajiwara, Tomoyuki and
Tanikawa, Chihiro and
Shimizu, Yuujin and
Nagahara, Hajime and
Ninomiya, Takashi",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.21",
doi = "10.18653/v1/2023.clinicalnlp-1.21",
pages = "156--160",
abstract = "We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ohtsuka-etal-2023-automated">
<titleInfo>
<title>Automated Orthodontic Diagnosis from a Summary of Medical Findings</title>
</titleInfo>
<name type="personal">
<namePart type="given">Takumi</namePart>
<namePart type="family">Ohtsuka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomoyuki</namePart>
<namePart type="family">Kajiwara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chihiro</namePart>
<namePart type="family">Tanikawa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuujin</namePart>
<namePart type="family">Shimizu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hajime</namePart>
<namePart type="family">Nagahara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Takashi</namePart>
<namePart type="family">Ninomiya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th 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">Asma</namePart>
<namePart type="family">Ben Abacha</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">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.</abstract>
<identifier type="citekey">ohtsuka-etal-2023-automated</identifier>
<identifier type="doi">10.18653/v1/2023.clinicalnlp-1.21</identifier>
<location>
<url>https://aclanthology.org/2023.clinicalnlp-1.21</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>156</start>
<end>160</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Automated Orthodontic Diagnosis from a Summary of Medical Findings
%A Ohtsuka, Takumi
%A Kajiwara, Tomoyuki
%A Tanikawa, Chihiro
%A Shimizu, Yuujin
%A Nagahara, Hajime
%A Ninomiya, Takashi
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ohtsuka-etal-2023-automated
%X We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.
%R 10.18653/v1/2023.clinicalnlp-1.21
%U https://aclanthology.org/2023.clinicalnlp-1.21
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.21
%P 156-160
Markdown (Informal)
[Automated Orthodontic Diagnosis from a Summary of Medical Findings](https://aclanthology.org/2023.clinicalnlp-1.21) (Ohtsuka et al., ClinicalNLP 2023)
ACL