Health policyholder clustering using medical consumption

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<title>Health policyholder clustering using medical consumption</title>
</titleInfo>
<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20220004602">
<namePart>Gauchon, Romain</namePart>
<nameIdentifier>MAPA20220004602</nameIdentifier>
</name>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20100059609">
<namePart>Loisel, Stéphane</namePart>
<nameIdentifier>MAPA20100059609</nameIdentifier>
</name>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20220004619">
<namePart>Rullière, Jean-Louis</namePart>
<nameIdentifier>MAPA20220004619</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2020</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">On paper, prevention appears to be a good complement to health insurance. However, its implementation is often costly. To maximize the impact and efficiency of prevention plans, plans should target particular groups of policyholders. In this article, we propose a way of clustering policyholders that could be a starting point for the targeting of prevention plans. This two-step method considers mainly policyholder health consumption for classification. The dimension is first reduced using a nonnegative matrix factorization algorithm, producing intermediate health product clusters. Policyholders are then clustered using Kohonen's map algorithm. This leads to a natural visualization of the results, allowing the simple comparison of results from different databases. The method is applied to two real French health insurer datasets. The method is shown to be easily understandable and able to cluster most policyholders efficiently.

</abstract>
<note type="statement of responsibility">Romain Gauchon, Stéphane Loisel, Jean-Louis Rullière </note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080573867">
<topic>Seguro de salud</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20210014192">
<topic>Prevención</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080579258">
<topic>Cálculo actuarial</topic>
</subject>
<classification authority="">344.1</classification>
<relatedItem type="host">
<titleInfo>
<title>European Actuarial Journal</title>
</titleInfo>
<originInfo>
<publisher>Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</publisher>
</originInfo>
<identifier type="local">MAP20220007085</identifier>
<part>
<text>07/12/2020 Volúmen 10 - Número 2 - diciembre 2020 , p. 599-626</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">220504</recordCreationDate>
<recordChangeDate encoding="iso8601">20220504130529.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20220013406</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>