%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 24
%N 1
%P e31528
%T A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation
%A Renner,Simon
%A Marty,Tom
%A Khadhar,Mickaïl
%A Foulquié,Pierre
%A Voillot,Paméla
%A Mebarki,Adel
%A Montagni,Ilaria
%A Texier,Nathalie
%A Schück,Stéphane
%+ Kap Code, 4 Rue de Cléry, Paris, 75002, France, 33 9 72 60 57 63, simon.renner@kapcode.fr
%K health-related quality of life
%K social media use
%K measures
%K real world
%K natural language processing
%K social media
%K NLP
%K infoveillance
%K quality of life
%K digital health
%K social listening
%D 2022
%7 28.1.2022
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 35089152
%R 10.2196/31528
%U https://www.jmir.org/2022/1/e31528
%U https://doi.org/10.2196/31528
%U http://www.ncbi.nlm.nih.gov/pubmed/35089152