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dc.rights.licenseopenen_US
dc.contributor.authorRENNER, Simon
dc.contributor.authorMARTY, Tom
dc.contributor.authorKHADHAR, Mickail
dc.contributor.authorFOULQUIE, Pierre
dc.contributor.authorVOILLOT, Pamela
dc.contributor.authorMEBARKI, Adel
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMONTAGNI, Ilaria
ORCID: 0000-0003-0076-0010
IDREF: 258573880
dc.contributor.authorTEXIER, Nathalie
dc.contributor.authorSCHUCK, Stephane
dc.date.accessioned2022-02-16T15:44:22Z
dc.date.available2022-02-16T15:44:22Z
dc.date.issued2022-01-28
dc.identifier.issn1438-8871 (Electronic) 1438-8871 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/124748
dc.description.abstractEnBACKGROUND: Monitoring social media has been shown to be a useful means to capture patients' opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life (HRQoL) is a useful indicator of overall patients' health, which can be captured online. OBJECTIVE: This study aimed to describe a social media listening algorithm able to detect the impact of diseases or treatments on specific dimensions of HRQoL based on posts written by patients in social media and forums. METHODS: Using a web crawler, 19 forums in France were harvested, and messages related to patients' experience with disease or treatment were specifically collected. The SF-36 (Short Form Health Survey) and EQ-5D (Euro Quality of Life 5 Dimensions) HRQoL surveys were mixed and adapted for a tailored social media listening system. This was carried out to better capture the variety of expression on social media, resulting in 5 dimensions of the HRQoL, which are physical, psychological, activity-based, social, and financial. Models were trained using cross-validation and hyperparameter optimization. Oversampling was used to increase the infrequent dimension: after annotation, SMOTE (synthetic minority oversampling technique) was used to balance the proportions of the dimensions among messages. RESULTS: The training set was composed of 1399 messages, randomly taken from a batch of 20,000 health-related messages coming from forums. The algorithm was able to detect a general impact on HRQoL (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70), and a financial impact (0.79 and 0.74). CONCLUSIONS: The development of an innovative method to extract health data from social media as real time assessment of patients' HRQoL is useful to a patient-centered medical care. As a source of real-world data, social media provide a complementary point of view to understand patients' concerns and unmet needs, as well as shedding light on how diseases and treatments can be a burden in their daily lives.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enHealth-related quality of life (12)
dc.subject.enSocial media use (3)
dc.subject.enMeasures (1)
dc.subject.enReal world (2)
dc.subject.enNatural language processing (103)
dc.subject.enSocial media (326)
dc.subject.enNLP
dc.subject.enInfoveillance (76)
dc.subject.enQuality of life (46)
dc.subject.enDigital health (270)
dc.subject.enSocial listening (2)
dc.title.enA New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation
dc.typeArticle de revueen_US
dc.identifier.doi10.2196/31528en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed35089152en_US
bordeaux.journalJournal of Medical Internet Researchen_US
bordeaux.volume24en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamHEALTHY_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03577423
hal.version1
hal.date.transferred2022-02-16T15:44:25Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
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