@Article{info:doi/10.2196/31528，作者=“Renner, Simon and Marty, Tom and Khadhar, Micka{\ ' i}l and Foulqui{\'e}， Pierre and vollot, Pam{\'e}la and Mebarki, Adel and Montagni, Ilaria and Texier, Nathalie and Sch{\ 'e} ck, St{\'e}phane”，标题=“从社交媒体证词中提取健康相关生活质量数据的新方法:算法开发与验证”，期刊=“J Med Internet Res”，年=“2022”，月=“Jan”，日=“28”，卷=“24”，号=“1”，页=“e31528”，关键词=“健康相关生活质量”;使用社交媒体;措施;现实世界;自然语言处理;社交媒体;NLP;infoveillance;生活质量; digital health; social listening", abstract="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. ", issn="1438-8871", doi="10.2196/31528", url="//www.mybigtv.com/2022/1/e31528", url="https://doi.org/10.2196/31528", url="http://www.ncbi.nlm.nih.gov/pubmed/35089152" }