@inproceedings{baillargeon-lamontagne-2024-smartr,
title = "{SMARTR}: A Framework for Early Detection using Survival Analysis of Longitudinal Texts",
author = "Baillargeon, Jean-Thomas and
Lamontagne, Luc",
editor = "Cao, Yang (Trista) and
Papadimitriou, Isabel and
Ovalle, Anaelia",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-srw.5",
pages = "36--41",
abstract = "This paper presents an innovative approach to the early detection of expensive insurance claims by leveraging survival analysis concepts within a deep learning framework exploiting textual information from claims notes. Our proposed SMARTR model addresses limitations of state-of-the-art models, such as handling data-label mismatches and non-uniform data frequency, to enhance a posteriori classification and early detection. Our results suggest that incorporating temporal dynamics and empty period representation improves model performance, highlighting the importance of considering time in insurance claim analysis. The approach appears promising for application to other insurance datasets.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="baillargeon-lamontagne-2024-smartr">
<titleInfo>
<title>SMARTR: A Framework for Early Detection using Survival Analysis of Longitudinal Texts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jean-Thomas</namePart>
<namePart type="family">Baillargeon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luc</namePart>
<namePart type="family">Lamontagne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="given">(Trista)</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Isabel</namePart>
<namePart type="family">Papadimitriou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anaelia</namePart>
<namePart type="family">Ovalle</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper presents an innovative approach to the early detection of expensive insurance claims by leveraging survival analysis concepts within a deep learning framework exploiting textual information from claims notes. Our proposed SMARTR model addresses limitations of state-of-the-art models, such as handling data-label mismatches and non-uniform data frequency, to enhance a posteriori classification and early detection. Our results suggest that incorporating temporal dynamics and empty period representation improves model performance, highlighting the importance of considering time in insurance claim analysis. The approach appears promising for application to other insurance datasets.</abstract>
<identifier type="citekey">baillargeon-lamontagne-2024-smartr</identifier>
<location>
<url>https://aclanthology.org/2024.naacl-srw.5</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>36</start>
<end>41</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SMARTR: A Framework for Early Detection using Survival Analysis of Longitudinal Texts
%A Baillargeon, Jean-Thomas
%A Lamontagne, Luc
%Y Cao, Yang (Trista)
%Y Papadimitriou, Isabel
%Y Ovalle, Anaelia
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F baillargeon-lamontagne-2024-smartr
%X This paper presents an innovative approach to the early detection of expensive insurance claims by leveraging survival analysis concepts within a deep learning framework exploiting textual information from claims notes. Our proposed SMARTR model addresses limitations of state-of-the-art models, such as handling data-label mismatches and non-uniform data frequency, to enhance a posteriori classification and early detection. Our results suggest that incorporating temporal dynamics and empty period representation improves model performance, highlighting the importance of considering time in insurance claim analysis. The approach appears promising for application to other insurance datasets.
%U https://aclanthology.org/2024.naacl-srw.5
%P 36-41
Markdown (Informal)
[SMARTR: A Framework for Early Detection using Survival Analysis of Longitudinal Texts](https://aclanthology.org/2024.naacl-srw.5) (Baillargeon & Lamontagne, NAACL 2024)
ACL