Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.
Identifieur interne : 000247 ( Main/Exploration ); précédent : 000246; suivant : 000248Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.
Auteurs : Alfredo Raglio [Italie] ; Marcello Imbriani [Italie] ; Chiara Imbriani [Italie] ; Paola Baiardi [Italie] ; Sara Manzoni [Italie] ; Marta Gianotti [Italie] ; Mauro Castelli [Portugal] ; Leonardo Vanneschi [Portugal] ; Francisco Vico [Espagne] ; Luca Manzoni [Italie]Source :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2020.
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Abstract
BACKGROUND
The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods.
METHODS
Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation.
RESULTS
A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency.
CONCLUSIONS
The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.
DOI: 10.1016/j.cmpb.2019.105160
PubMed: 31710983
Affiliations:
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Electronic address: lvanneschi@novaims.unl.pt.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312</wicri:regionArea><wicri:noRegion>Lisboa 1070-312</wicri:noRegion></affiliation></author><author><name sortKey="Vico, Francisco" sort="Vico, Francisco" uniqKey="Vico F" first="Francisco" last="Vico">Francisco Vico</name><affiliation wicri:level="4"><nlm:affiliation>ETSI Informatica, Andalucia Tech, University of Malaga, Malaga 29071, Spain. Electronic address: fjvico@uma.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>ETSI Informatica, Andalucia Tech, University of Malaga, Malaga 29071</wicri:regionArea><placeName><region nuts="2" type="communauté">Andalousie</region><settlement type="city">Malaga</settlement></placeName><orgName type="university">Université de Malaga</orgName></affiliation></author><author><name sortKey="Manzoni, Luca" sort="Manzoni, Luca" uniqKey="Manzoni L" first="Luca" last="Manzoni">Luca Manzoni</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, via Weiss 2, 34128 Trieste, Italy. 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Electronic address: alfredo.raglio@icsmaugeri.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100</wicri:regionArea><wicri:noRegion>Pavia 27100</wicri:noRegion></affiliation></author><author><name sortKey="Imbriani, Marcello" sort="Imbriani, Marcello" uniqKey="Imbriani M" first="Marcello" last="Imbriani">Marcello Imbriani</name><affiliation wicri:level="1"><nlm:affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: marcello.imbriani@icsmaugeri.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100</wicri:regionArea><wicri:noRegion>Pavia 27100</wicri:noRegion></affiliation></author><author><name sortKey="Imbriani, Chiara" sort="Imbriani, Chiara" uniqKey="Imbriani C" first="Chiara" last="Imbriani">Chiara Imbriani</name><affiliation wicri:level="1"><nlm:affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: chiara.imbriani@icsmaugeri.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100</wicri:regionArea><wicri:noRegion>Pavia 27100</wicri:noRegion></affiliation></author><author><name sortKey="Baiardi, Paola" sort="Baiardi, Paola" uniqKey="Baiardi P" first="Paola" last="Baiardi">Paola Baiardi</name><affiliation wicri:level="1"><nlm:affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: paola.baiardi@icsmaugeri.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100</wicri:regionArea><wicri:noRegion>Pavia 27100</wicri:noRegion></affiliation></author><author><name sortKey="Manzoni, Sara" sort="Manzoni, Sara" uniqKey="Manzoni S" first="Sara" last="Manzoni">Sara Manzoni</name><affiliation wicri:level="1"><nlm:affiliation>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048</wicri:regionArea><wicri:noRegion>Sospiro (CR) 26048</wicri:noRegion></affiliation></author><author><name sortKey="Gianotti, Marta" sort="Gianotti, Marta" uniqKey="Gianotti M" first="Marta" last="Gianotti">Marta Gianotti</name><affiliation wicri:level="1"><nlm:affiliation>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048, Italy. Electronic address: marta.gianotti@fondazionesospiro.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048</wicri:regionArea><wicri:noRegion>Sospiro (CR) 26048</wicri:noRegion></affiliation></author><author><name sortKey="Castelli, Mauro" sort="Castelli, Mauro" uniqKey="Castelli M" first="Mauro" last="Castelli">Mauro Castelli</name><affiliation wicri:level="1"><nlm:affiliation>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, Portugal. Electronic address: mcastelli@novaims.unl.pt.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312</wicri:regionArea><wicri:noRegion>Lisboa 1070-312</wicri:noRegion></affiliation></author><author><name sortKey="Vanneschi, Leonardo" sort="Vanneschi, Leonardo" uniqKey="Vanneschi L" first="Leonardo" last="Vanneschi">Leonardo Vanneschi</name><affiliation wicri:level="1"><nlm:affiliation>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, Portugal. Electronic address: lvanneschi@novaims.unl.pt.</nlm:affiliation><country xml:lang="fr">Portugal</country><wicri:regionArea>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312</wicri:regionArea><wicri:noRegion>Lisboa 1070-312</wicri:noRegion></affiliation></author><author><name sortKey="Vico, Francisco" sort="Vico, Francisco" uniqKey="Vico F" first="Francisco" last="Vico">Francisco Vico</name><affiliation wicri:level="4"><nlm:affiliation>ETSI Informatica, Andalucia Tech, University of Malaga, Malaga 29071, Spain. Electronic address: fjvico@uma.es.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>ETSI Informatica, Andalucia Tech, University of Malaga, Malaga 29071</wicri:regionArea><placeName><region nuts="2" type="communauté">Andalousie</region><settlement type="city">Malaga</settlement></placeName><orgName type="university">Université de Malaga</orgName></affiliation></author><author><name sortKey="Manzoni, Luca" sort="Manzoni, Luca" uniqKey="Manzoni L" first="Luca" last="Manzoni">Luca Manzoni</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, via Weiss 2, 34128 Trieste, Italy. Electronic address: luca.manzoni@disco.unimib.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, via Weiss 2, 34128 Trieste</wicri:regionArea><wicri:noRegion>34128 Trieste</wicri:noRegion></affiliation></author></analytic><series><title level="j">Computer methods and programs in biomedicine</title><idno type="eISSN">1872-7565</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Child (MeSH)</term><term>Child, Preschool (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Infant (MeSH)</term><term>Machine Learning (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Music Therapy (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent (MeSH)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Apprentissage machine (MeSH)</term><term>Enfant (MeSH)</term><term>Enfant d'âge préscolaire (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Musicothérapie (MeSH)</term><term>Mâle (MeSH)</term><term>Nourrisson (MeSH)</term><term>Sujet âgé (MeSH)</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Child</term><term>Child, Preschool</term><term>Female</term><term>Humans</term><term>Infant</term><term>Machine Learning</term><term>Male</term><term>Middle Aged</term><term>Music Therapy</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Apprentissage machine</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Femelle</term><term>Humains</term><term>Musicothérapie</term><term>Mâle</term><term>Nourrisson</term><term>Sujet âgé</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">31710983</PMID><DateCompleted><Year>2021</Year><Month>01</Month><Day>06</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>06</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1872-7565</ISSN><JournalIssue CitedMedium="Internet"><Volume>185</Volume><PubDate><Year>2020</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Computer methods and programs in biomedicine</Title><ISOAbbreviation>Comput Methods Programs Biomed</ISOAbbreviation></Journal><ArticleTitle>Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial.</ArticleTitle><Pagination><MedlinePgn>105160</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0169-2607(19)30155-5</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.cmpb.2019.105160</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.</AbstractText><CopyrightInformation>Copyright © 2019. Published by Elsevier B.V.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Raglio</LastName><ForeName>Alfredo</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: alfredo.raglio@icsmaugeri.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Imbriani</LastName><ForeName>Marcello</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: marcello.imbriani@icsmaugeri.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Imbriani</LastName><ForeName>Chiara</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: chiara.imbriani@icsmaugeri.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baiardi</LastName><ForeName>Paola</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Istituti Clinici Scientifici Maugeri IRCCS, Via Boezio 28, Pavia 27100, Italy. Electronic address: paola.baiardi@icsmaugeri.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Manzoni</LastName><ForeName>Sara</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gianotti</LastName><ForeName>Marta</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Fondazione Istituto Ospedaliero di Sospiro, Sospiro (CR) 26048, Italy. Electronic address: marta.gianotti@fondazionesospiro.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Castelli</LastName><ForeName>Mauro</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, Portugal. Electronic address: mcastelli@novaims.unl.pt.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vanneschi</LastName><ForeName>Leonardo</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, Portugal. Electronic address: lvanneschi@novaims.unl.pt.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vico</LastName><ForeName>Francisco</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>ETSI Informatica, Andalucia Tech, University of Malaga, Malaga 29071, Spain. Electronic address: fjvico@uma.es.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Manzoni</LastName><ForeName>Luca</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milan, Italy; Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, via Weiss 2, 34128 Trieste, Italy. Electronic address: luca.manzoni@disco.unimib.it.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016449">Randomized Controlled Trial</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>10</Month><Day>29</Day></ArticleDate></Article><MedlineJournalInfo><Country>Ireland</Country><MedlineTA>Comput Methods Programs Biomed</MedlineTA><NlmUniqueID>8506513</NlmUniqueID><ISSNLinking>0169-2607</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007223" MajorTopicYN="N">Infant</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009147" MajorTopicYN="Y">Music Therapy</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Decision tree methods</Keyword><Keyword MajorTopicYN="N">Machine learning techniques</Keyword><Keyword MajorTopicYN="N">Medicine</Keyword><Keyword MajorTopicYN="N">Therapeutic music listening</Keyword><Keyword MajorTopicYN="N">Therapeutic predictivity</Keyword></KeywordList><CoiStatement>Declaration of Competing Interest None.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>01</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>09</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>10</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>11</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2021</Year><Month>1</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>11</Month><Day>12</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">31710983</ArticleId><ArticleId IdType="pii">S0169-2607(19)30155-5</ArticleId><ArticleId IdType="doi">10.1016/j.cmpb.2019.105160</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li><li>Italie</li><li>Portugal</li></country><region><li>Andalousie</li></region><settlement><li>Malaga</li></settlement><orgName><li>Université de Malaga</li></orgName></list><tree><country name="Italie"><noRegion><name sortKey="Raglio, Alfredo" sort="Raglio, Alfredo" uniqKey="Raglio A" first="Alfredo" last="Raglio">Alfredo Raglio</name></noRegion><name sortKey="Baiardi, Paola" sort="Baiardi, Paola" uniqKey="Baiardi P" first="Paola" last="Baiardi">Paola Baiardi</name><name sortKey="Gianotti, Marta" sort="Gianotti, Marta" uniqKey="Gianotti M" first="Marta" last="Gianotti">Marta Gianotti</name><name sortKey="Imbriani, Chiara" sort="Imbriani, Chiara" uniqKey="Imbriani C" first="Chiara" last="Imbriani">Chiara Imbriani</name><name sortKey="Imbriani, Marcello" sort="Imbriani, Marcello" uniqKey="Imbriani M" first="Marcello" last="Imbriani">Marcello Imbriani</name><name sortKey="Manzoni, Luca" sort="Manzoni, Luca" uniqKey="Manzoni L" first="Luca" last="Manzoni">Luca Manzoni</name><name sortKey="Manzoni, Sara" sort="Manzoni, Sara" uniqKey="Manzoni S" first="Sara" last="Manzoni">Sara Manzoni</name></country><country name="Portugal"><noRegion><name sortKey="Castelli, Mauro" sort="Castelli, Mauro" uniqKey="Castelli M" first="Mauro" last="Castelli">Mauro Castelli</name></noRegion><name sortKey="Vanneschi, Leonardo" sort="Vanneschi, Leonardo" uniqKey="Vanneschi L" first="Leonardo" last="Vanneschi">Leonardo Vanneschi</name></country><country name="Espagne"><region name="Andalousie"><name sortKey="Vico, Francisco" sort="Vico, Francisco" uniqKey="Vico F" first="Francisco" last="Vico">Francisco Vico</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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