| CARVIEW |
Select Language
HTTP/2 301
date: Mon, 19 Jan 2026 04:48:36 GMT
content-length: 0
location: https://doi.org/10.18653/V1/W17-2342
server: cloudflare
vary: Origin
expires: Tue, 20 Jan 2026 04:48:36 GMT
permissions-policy: interest-cohort=(),browsing-topics=()
cf-cache-status: DYNAMIC
nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800}
strict-transport-security: max-age=31536000; includeSubDomains; preload
report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=UUGrmR67fB99qr8eynnVoVebpiPwBeCpReIZursquZAw7UCjOvBNG9zS9QOirNUKCj2HDL0Q9fGSOLB%2Fwg%2FMh0kB%2FC7zVA%3D%3D"}]}
cf-ray: 9c03b365696e8087-BLR
alt-svc: h3=":443"; ma=86400
HTTP/2 302
date: Mon, 19 Jan 2026 04:48:36 GMT
content-type: text/html;charset=utf-8
location: https://aclweb.org/anthology/W17-2342
server: cloudflare
vary: Origin
vary: Accept
expires: Mon, 19 Jan 2026 05:11:01 GMT
permissions-policy: interest-cohort=(),browsing-topics=()
cf-cache-status: DYNAMIC
nel: {"report_to":"cf-nel","success_fraction":0.0,"max_age":604800}
strict-transport-security: max-age=31536000; includeSubDomains; preload
report-to: {"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=68iH%2B3mwV6PRgkZFsFOD17S8RdQV3jcrefnjRcaUqRbfFlVv8hF0Rng07eQDjPBgeaqHDgGbXzov3CK7Q301acvB7VzX4A%3D%3D"}]}
cf-ray: 9c03b366bb478087-BLR
alt-svc: h3=":443"; ma=86400
HTTP/1.1 301 Moved Permanently
Date: Mon, 19 Jan 2026 04:48:37 GMT
Server: Apache
Location: https://aclweb.org/anthology/W17-2342
Content-Length: 245
Content-Type: text/html; charset=iso-8859-1
HTTP/2 301
date: Mon, 19 Jan 2026 04:48:38 GMT
server: nginx/1.25.5
content-type: text/html; charset=iso-8859-1
content-length: 241
location: https://aclanthology.org/W17-2342
x-server-cache: true
x-proxy-cache: MISS
host-header: c2hhcmVkLmJsdWVob3N0LmNvbQ==
HTTP/1.1 301 Moved Permanently
Date: Mon, 19 Jan 2026 04:48:38 GMT
Server: Apache/2.4.41 (Ubuntu)
Location: https://aclanthology.org/W17-2342/
Content-Length: 325
Content-Type: text/html; charset=iso-8859-1
HTTP/1.1 200 OK
Date: Mon, 19 Jan 2026 04:48:39 GMT
Server: Apache/2.4.41 (Ubuntu)
Last-Modified: Sun, 18 Jan 2026 11:49:45 GMT
ETag: "8802-648a82bc02d87-gzip"
Accept-Ranges: bytes
Vary: Accept-Encoding
Content-Encoding: gzip
Content-Length: 8493
Content-Type: text/html; charset=utf-8
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods - ACL Anthology
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, Anthony Nguyen
Correct Metadata for
Abstract
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.- Anthology ID:
- W17-2342
- Volume:
- Proceedings of the 16th BioNLP Workshop
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 328–332
- Language:
- URL:
- https://aclanthology.org/W17-2342/
- DOI:
- 10.18653/v1/W17-2342
- Bibkey:
- Cite (ACL):
- Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, and Anthony Nguyen. 2017. Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods. In Proceedings of the 16th BioNLP Workshop, pages 328–332, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods (Karimi et al., BioNLP 2017)
- Copy Citation:
- PDF:
- https://aclanthology.org/W17-2342.pdf
Export citation
@inproceedings{karimi-etal-2017-automatic,
title = "Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods",
author = "Karimi, Sarvnaz and
Dai, Xiang and
Hassanzadeh, Hamed and
Nguyen, Anthony",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2342/",
doi = "10.18653/v1/W17-2342",
pages = "328--332",
abstract = "Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="https://www.loc.gov/mods/v3">
<mods ID="karimi-etal-2017-automatic">
<titleInfo>
<title>Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sarvnaz</namePart>
<namePart type="family">Karimi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiang</namePart>
<namePart type="family">Dai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamed</namePart>
<namePart type="family">Hassanzadeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anthony</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 16th BioNLP Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="given">Bretonnel</namePart>
<namePart type="family">Cohen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dina</namePart>
<namePart type="family">Demner-Fushman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sophia</namePart>
<namePart type="family">Ananiadou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junichi</namePart>
<namePart type="family">Tsujii</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, Canada,</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.</abstract>
<identifier type="citekey">karimi-etal-2017-automatic</identifier>
<identifier type="doi">10.18653/v1/W17-2342</identifier>
<location>
<url>https://aclanthology.org/W17-2342/</url>
</location>
<part>
<date>2017-08</date>
<extent unit="page">
<start>328</start>
<end>332</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings %T Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods %A Karimi, Sarvnaz %A Dai, Xiang %A Hassanzadeh, Hamed %A Nguyen, Anthony %Y Cohen, Kevin Bretonnel %Y Demner-Fushman, Dina %Y Ananiadou, Sophia %Y Tsujii, Junichi %S Proceedings of the 16th BioNLP Workshop %D 2017 %8 August %I Association for Computational Linguistics %C Vancouver, Canada, %F karimi-etal-2017-automatic %X Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods. %R 10.18653/v1/W17-2342 %U https://aclanthology.org/W17-2342/ %U https://doi.org/10.18653/v1/W17-2342 %P 328-332
Markdown (Informal)
[Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods](https://aclanthology.org/W17-2342/) (Karimi et al., BioNLP 2017)
- Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods (Karimi et al., BioNLP 2017)
ACL
- Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, and Anthony Nguyen. 2017. Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods. In Proceedings of the 16th BioNLP Workshop, pages 328–332, Vancouver, Canada,. Association for Computational Linguistics.