A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation
| dc.rights.license | open | en_US |
| dc.contributor.author | RENNER, Simon | |
| dc.contributor.author | MARTY, Tom | |
| dc.contributor.author | KHADHAR, Mickail | |
| dc.contributor.author | FOULQUIE, Pierre | |
| dc.contributor.author | VOILLOT, Pamela | |
| dc.contributor.author | MEBARKI, Adel | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | MONTAGNI, Ilaria
ORCID: 0000-0003-0076-0010 IDREF: 258573880 | |
| dc.contributor.author | TEXIER, Nathalie | |
| dc.contributor.author | SCHUCK, Stephane | |
| dc.date.accessioned | 2022-02-16T15:44:22Z | |
| dc.date.available | 2022-02-16T15:44:22Z | |
| dc.date.issued | 2022-01-28 | |
| dc.identifier.issn | 1438-8871 (Electronic) 1438-8871 (Linking) | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/124748 | |
| dc.description.abstractEn | BACKGROUND: 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.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject.en | Health-related quality of life (12) | |
| dc.subject.en | Social media use (3) | |
| dc.subject.en | Measures (1) | |
| dc.subject.en | Real world (2) | |
| dc.subject.en | Natural language processing (103) | |
| dc.subject.en | Social media (326) | |
| dc.subject.en | NLP | |
| dc.subject.en | Infoveillance (76) | |
| dc.subject.en | Quality of life (46) | |
| dc.subject.en | Digital health (270) | |
| dc.subject.en | Social listening (2) | |
| dc.title.en | A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.2196/31528 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 35089152 | en_US |
| bordeaux.journal | Journal of Medical Internet Research | en_US |
| bordeaux.volume | 24 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 1 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | HEALTHY_BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.identifier | hal-03577423 | |
| hal.version | 1 | |
| hal.date.transferred | 2022-02-16T15:44:25Z | |
| hal.export | true | |
| dc.rights.cc | Pas de Licence CC | en_US |
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