Improving healthcare cost prediction for chronic disease through covariate clustering and subgroup analysis methods

<?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>Improving healthcare cost prediction for chronic disease through covariate clustering and subgroup analysis methods</title>
</titleInfo>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20260001302">
<namePart>Huang, Yifan</namePart>
<nameIdentifier>MAPA20260001302</nameIdentifier>
</name>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20260001319">
<namePart>Cao, Yang</namePart>
<nameIdentifier>MAPA20260001319</nameIdentifier>
</name>
<name type="corporate" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20100017661">
<namePart>International Actuarial Association</namePart>
<nameIdentifier>MAPA20100017661</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">bel</placeTerm>
</place>
<dateIssued encoding="marc">2025</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">eng</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">Predicting healthcare costs for chronic diseases is difficult because these costs depend not only on medical factors but also on patients' own perceptions and behaviors. To better capture this complexity, this paper introduces a new statistical framework that combines covariate clustering with finite mixture regression models. This approach groups highly related variables and identifies patient subgroups, improving both model interpretability and prediction accuracy in high-dimensional, noisy, and correlated data settings. The method uses a penalized clustering structure and a dedicated EMADMM algorithm to handle the challenging optimization problem. Through simulations and real-world data from diabetes patients, the framework shows strong stability and effectiveness: it improves predictions by sharing information across related variables and reveals meaningful behavioral patterns in patients' self-perception data.</abstract>
<note type="statement of responsibility">Zhengxiao Li, Yifan Huang and Yang Cao</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080602437">
<topic>Matemática del seguro</topic>
</subject>
<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="MAPA20080601232">
<topic>Enfermedades crónicas</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080579784">
<topic>Costes económicos</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20120011137">
<topic>Predicciones estadísticas</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080597733">
<topic>Modelos estadísticos</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080602642">
<topic>Modelos de simulación</topic>
</subject>
<classification authority="">6</classification>
<relatedItem type="host">
<titleInfo>
<title>Astin bulletin</title>
</titleInfo>
<originInfo>
<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
</originInfo>
<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
<part>
<text>12/05/2025 Volume 55 Issue 2 - may 2025 , p. 375 - 394</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">260130</recordCreationDate>
<recordChangeDate encoding="iso8601">20260202103507.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20260001739</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>