@inproceedings{wunnava-etal-2024-development,
title = "Development of a Benchmark Corpus for Medical Device Adverse Event Detection",
author = "Wunnava, Susmitha and
Harris, David A. and
Bourgeois, Florence T. and
Miller, Timothy A.",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Thompson, Paul and
Ondov, Brian",
booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.cl4health-1.29",
pages = "240--245",
abstract = "The U.S. Food and Drug Administration (FDA) collects real-world adverse events, including device-associated deaths, injuries, and malfunctions, through passive reporting to the agency{'}s Manufacturer and User Facility Device Experience (MAUDE) database. However, this system{'}s full potential remains untapped given the extensive use of unstructured text in medical device adverse event reports and lack of FDA resources and expertise to properly analyze all available data. In this work, we focus on addressing this limitation through the development of an annotated benchmark corpus to support the design and development of state-of-the-art NLP approaches towards automatic extraction of device-related adverse event information from FDA Medical Device Adverse Event Reports. We develop a dataset of labeled medical device reports from a diverse set of high-risk device types, that can be used for supervised machine learning. We develop annotation guidelines and manually annotate for nine entity types. The resulting dataset contains 935 annotated adverse event reports, containing 12252 annotated spans across the nine entity types. The dataset developed in this work will be made publicly available upon publication.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wunnava-etal-2024-development">
<titleInfo>
<title>Development of a Benchmark Corpus for Medical Device Adverse Event Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Susmitha</namePart>
<namePart type="family">Wunnava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="given">A</namePart>
<namePart type="family">Harris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Florence</namePart>
<namePart type="given">T</namePart>
<namePart type="family">Bourgeois</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Timothy</namePart>
<namePart type="given">A</namePart>
<namePart type="family">Miller</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024</title>
</titleInfo>
<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">Paul</namePart>
<namePart type="family">Thompson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brian</namePart>
<namePart type="family">Ondov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The U.S. Food and Drug Administration (FDA) collects real-world adverse events, including device-associated deaths, injuries, and malfunctions, through passive reporting to the agency’s Manufacturer and User Facility Device Experience (MAUDE) database. However, this system’s full potential remains untapped given the extensive use of unstructured text in medical device adverse event reports and lack of FDA resources and expertise to properly analyze all available data. In this work, we focus on addressing this limitation through the development of an annotated benchmark corpus to support the design and development of state-of-the-art NLP approaches towards automatic extraction of device-related adverse event information from FDA Medical Device Adverse Event Reports. We develop a dataset of labeled medical device reports from a diverse set of high-risk device types, that can be used for supervised machine learning. We develop annotation guidelines and manually annotate for nine entity types. The resulting dataset contains 935 annotated adverse event reports, containing 12252 annotated spans across the nine entity types. The dataset developed in this work will be made publicly available upon publication.</abstract>
<identifier type="citekey">wunnava-etal-2024-development</identifier>
<location>
<url>https://aclanthology.org/2024.cl4health-1.29</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>240</start>
<end>245</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Development of a Benchmark Corpus for Medical Device Adverse Event Detection
%A Wunnava, Susmitha
%A Harris, David A.
%A Bourgeois, Florence T.
%A Miller, Timothy A.
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F wunnava-etal-2024-development
%X The U.S. Food and Drug Administration (FDA) collects real-world adverse events, including device-associated deaths, injuries, and malfunctions, through passive reporting to the agency’s Manufacturer and User Facility Device Experience (MAUDE) database. However, this system’s full potential remains untapped given the extensive use of unstructured text in medical device adverse event reports and lack of FDA resources and expertise to properly analyze all available data. In this work, we focus on addressing this limitation through the development of an annotated benchmark corpus to support the design and development of state-of-the-art NLP approaches towards automatic extraction of device-related adverse event information from FDA Medical Device Adverse Event Reports. We develop a dataset of labeled medical device reports from a diverse set of high-risk device types, that can be used for supervised machine learning. We develop annotation guidelines and manually annotate for nine entity types. The resulting dataset contains 935 annotated adverse event reports, containing 12252 annotated spans across the nine entity types. The dataset developed in this work will be made publicly available upon publication.
%U https://aclanthology.org/2024.cl4health-1.29
%P 240-245
Markdown (Informal)
[Development of a Benchmark Corpus for Medical Device Adverse Event Detection](https://aclanthology.org/2024.cl4health-1.29) (Wunnava et al., CL4Health-WS 2024)
ACL